ECE374N/Final Project/overall_analysis.ipynb

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  {
   "cell_type": "markdown",
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   "source": [
    "# Motor Imagery Decoder — Train OFFLINE, Evaluate ONLINE (FES vs NOFES)\n",
    "\n",
    "For each subject × offline-session, train a CSP + LDA classifier on the OFFLINE recording, then apply it to the two matched ONLINE sessions.\n",
    "\n",
    "**Pair 1:** train on `S001 OFFLINE_FES` → test on `S002 ONLINE_FES` and `S003 ONLINE_NOFES`\n",
    "**Pair 2:** train on `S004 OFFLINE_NOFES` → test on `S006 ONLINE_FES` and `S005 ONLINE_NOFES`\n",
    "\n",
    "**Metrics reported per (subject × pair × condition):**\n",
    "1. **Classification accuracy** — fraction of cued trials correctly classified\n",
    "2. **Classification amplitude** — mean |LDA decision-function value|\n",
    "3. **SNR** — (a) Fisher ratio of the LDA projection on online data, and (b) mu-band power ratio REST / MI over motor channels C3/Cz/C4"
   ]
  },
  {
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   "execution_count": 2,
   "id": "578c9128",
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    "execution": {
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   "source": [
    "# Install dependencies if needed\n",
    "# !pip install pyxdf mne scipy numpy matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "857b22c0",
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   "source": [
    "import os\n",
    "import re\n",
    "import glob\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Patch\n",
    "import pyxdf\n",
    "from scipy.signal import welch, butter, filtfilt, iirnotch\n",
    "from scipy.linalg import eigh\n",
    "from scipy.stats import wasserstein_distance\n",
    "\n",
    "plt.rcParams.update({'font.size': 11, 'figure.dpi': 120})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe68bf0e",
   "metadata": {},
   "source": [
    "## Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dc4b2c55",
   "metadata": {
    "execution": {
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   "source": [
    "DATA_DIR = os.path.join(os.path.dirname(os.path.abspath('__file__')), 'Group 2 - Glove')\n",
    "\n",
    "# Marker codes (from experiment trigger table)\n",
    "MI_BEGIN       = 200\n",
    "MI_END         = 220\n",
    "MI_EARLYSTOP   = 240   # online only: live classifier fired → successful MI detection\n",
    "REST_BEGIN     = 100\n",
    "REST_END       = 120\n",
    "REST_EARLYSTOP = 140   # online only: live classifier fired → successful REST detection\n",
    "ROBOT_BEGIN    = 300\n",
    "ROBOT_END      = 320\n",
    "\n",
    "TARGET_MARKERS = [100, 120, 140, 200, 220, 240]\n",
    "\n",
    "T_PRE  = -1.0\n",
    "T_POST =  5.0\n",
    "\n",
    "# ── Preprocessing ────────────────────────────────────────────────────────────\n",
    "NOTCH_FREQ     = 60.0\n",
    "NOTCH_Q        = 30\n",
    "BP_LO, BP_HI   = 8.0, 30.0\n",
    "USE_CAR        = True\n",
    "PTP_REJECT_UV  = 100.0\n",
    "\n",
    "N_CSP = 4\n",
    "\n",
    "NON_EEG = {'AUX1', 'AUX2', 'AUX3', 'AUX7', 'AUX8', 'AUX9', 'TRIGGER'}\n",
    "RENAME  = {'FP1':'Fp1','FPZ':'Fpz','FP2':'Fp2','FZ':'Fz','CZ':'Cz',\n",
    "           'PZ':'Pz','POZ':'POz','OZ':'Oz'}\n",
    "\n",
    "MOTOR_CH = ['C3', 'Cz', 'C4']\n",
    "MU_BAND  = (8, 13)\n",
    "\n",
    "PAIRS = [\n",
    "    {'name':  'Pair1 (train=OFFLINE_FES)',\n",
    "     'train': 'S001', 'online_fes': 'S002', 'online_nofes': 'S003'},\n",
    "    {'name':  'Pair2 (train=OFFLINE_NOFES)',\n",
    "     'train': 'S004', 'online_fes': 'S006', 'online_nofes': 'S005'},\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21a40df3",
   "metadata": {},
   "source": [
    "## Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e798b039",
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   "source": [
    "# ── XDF loading + session parsing ─────────────────────────────────────────────\n",
    "\n",
    "def get_channel_names_from_xdf(eeg_stream):\n",
    "    ch_desc  = eeg_stream['info']['desc'][0]\n",
    "    channels = ch_desc.get('channels', [{}])[0].get('channel', [])\n",
    "    return [ch['label'][0] for ch in channels]\n",
    "\n",
    "\n",
    "_SESSION_RE = re.compile(r'ses-(S\\d+)(O[A-Z]*LINE)_(FES|NOFES)')\n",
    "_SUBJ_RE    = re.compile(r'SUBJ_(\\d+)')\n",
    "\n",
    "def parse_session(path):\n",
    "    base   = os.path.basename(path)\n",
    "    m_subj = _SUBJ_RE.search(base)\n",
    "    m_ses  = _SESSION_RE.search(base)\n",
    "    if not (m_subj and m_ses):\n",
    "        return None\n",
    "    ses_id, raw_kind, stim = m_ses.group(1), m_ses.group(2), m_ses.group(3)\n",
    "    kind = 'OFFLINE' if 'OFF' in raw_kind else 'ONLINE'\n",
    "    return m_subj.group(1), ses_id, kind, stim\n",
    "\n",
    "\n",
    "def load_xdf_file(filepath):\n",
    "    streams, _ = pyxdf.load_xdf(filepath)\n",
    "\n",
    "    eeg_stream = marker_stream = None\n",
    "    for s in streams:\n",
    "        stype = s['info']['type'][0].lower()\n",
    "        if stype == 'eeg':       eeg_stream    = s\n",
    "        elif stype == 'markers': marker_stream = s\n",
    "    if eeg_stream is None or marker_stream is None:\n",
    "        eeg_stream    = streams[0]\n",
    "        marker_stream = streams[1] if len(streams) > 1 else None\n",
    "\n",
    "    eeg_timestamps = np.array(eeg_stream['time_stamps'])\n",
    "    eeg_data       = np.array(eeg_stream['time_series']).T\n",
    "    channel_names  = get_channel_names_from_xdf(eeg_stream)\n",
    "    sfreq          = float(eeg_stream['info']['nominal_srate'][0])\n",
    "\n",
    "    valid_idx     = [i for i, ch in enumerate(channel_names) if ch not in NON_EEG]\n",
    "    channel_names = [channel_names[i] for i in valid_idx]\n",
    "    eeg_data      = eeg_data[valid_idx, :]\n",
    "    channel_names = [RENAME.get(ch, ch) for ch in channel_names]\n",
    "\n",
    "    ts_arr      = np.asarray(marker_stream['time_series'], dtype=float)\n",
    "    marker_data = ts_arr[:, 0].astype(int)\n",
    "    marker_ts   = ts_arr[:, 1]\n",
    "    keep        = np.isin(marker_data, TARGET_MARKERS)\n",
    "    return eeg_data, eeg_timestamps, marker_data[keep], marker_ts[keep], channel_names, sfreq\n",
    "\n",
    "\n",
    "# ── Preprocessing primitives ─────────────────────────────────────────────────\n",
    "\n",
    "def notch_filter(data, freq, sfreq, Q=NOTCH_Q):\n",
    "    b, a = iirnotch(freq, Q, fs=sfreq)\n",
    "    return filtfilt(b, a, data, axis=-1)\n",
    "\n",
    "\n",
    "def car(data):\n",
    "    return data - data.mean(axis=0, keepdims=True)\n",
    "\n",
    "\n",
    "def bandpass(data, lo, hi, sfreq, order=4):\n",
    "    nyq  = sfreq / 2.0\n",
    "    b, a = butter(order, [max(lo, 0.5) / nyq, min(hi, nyq - 0.1) / nyq], btype='band')\n",
    "    return filtfilt(b, a, data, axis=-1)\n",
    "\n",
    "\n",
    "def reject_by_ptp(X, thresh_uv=PTP_REJECT_UV):\n",
    "    if X.size == 0:\n",
    "        return np.zeros(0, dtype=bool)\n",
    "    ptp = X.max(axis=-1) - X.min(axis=-1)\n",
    "    return ptp.max(axis=-1) < thresh_uv\n",
    "\n",
    "\n",
    "def extract_epochs(eeg_data, eeg_ts, marker_data, marker_ts, sfreq, begin_code,\n",
    "                   t_pre=T_PRE, t_post=T_POST):\n",
    "    epochs = []\n",
    "    n_pre  = int(abs(t_pre) * sfreq)\n",
    "    for bi in np.where(marker_data == begin_code)[0]:\n",
    "        t_start = marker_ts[bi]\n",
    "        i0 = np.searchsorted(eeg_ts, t_start + t_pre)\n",
    "        i1 = np.searchsorted(eeg_ts, t_start + t_post)\n",
    "        if i0 < 0 or i1 > eeg_data.shape[1]:\n",
    "            continue\n",
    "        ep = eeg_data[:, i0:i1].copy()\n",
    "        if ep.shape[1] > n_pre:\n",
    "            ep -= ep[:, :n_pre].mean(axis=1, keepdims=True)\n",
    "        epochs.append(ep)\n",
    "    if not epochs:\n",
    "        return np.empty((0, eeg_data.shape[0], 0))\n",
    "    min_len = min(e.shape[-1] for e in epochs)\n",
    "    return np.stack([e[:, :min_len] for e in epochs])\n",
    "\n",
    "\n",
    "# ── Trial-level parsing from markers ─────────────────────────────────────────\n",
    "\n",
    "def trial_events(marker_data, marker_ts):\n",
    "    \"\"\"Parse marker stream into ordered per-trial records.\n",
    "    Returns list of dicts with keys: cls ('MI'|'REST'), success (bool),\n",
    "    latency (seconds from BEGIN to EARLYSTOP, or None on failure), t_begin.\n",
    "    \"\"\"\n",
    "    begin_codes = {MI_BEGIN: 'MI', REST_BEGIN: 'REST'}\n",
    "    early_of    = {'MI': MI_EARLYSTOP, 'REST': REST_EARLYSTOP}\n",
    "\n",
    "    begin_idx = np.where(np.isin(marker_data, list(begin_codes.keys())))[0]\n",
    "    trials = []\n",
    "    for i, bi in enumerate(begin_idx):\n",
    "        cls     = begin_codes[int(marker_data[bi])]\n",
    "        t_begin = float(marker_ts[bi])\n",
    "        # Search until next BEGIN (or end of stream) for EARLYSTOP of matching class\n",
    "        end    = begin_idx[i + 1] if i + 1 < len(begin_idx) else len(marker_data)\n",
    "        window = marker_data[bi + 1:end]\n",
    "        wts    = marker_ts[bi + 1:end]\n",
    "        hit    = np.where(window == early_of[cls])[0]\n",
    "        if len(hit):\n",
    "            trials.append(dict(cls=cls, success=True,\n",
    "                               latency=float(wts[hit[0]] - t_begin), t_begin=t_begin))\n",
    "        else:\n",
    "            trials.append(dict(cls=cls, success=False, latency=None, t_begin=t_begin))\n",
    "    return trials\n",
    "\n",
    "\n",
    "def marker_stats(marker_data, marker_ts):\n",
    "    \"\"\"Online performance summary from markers. Returns None for offline sessions.\n",
    "    Fields: mk_acc, mi_acc, rest_acc, mi_latency, rest_latency, trials (ordered).\n",
    "    \"\"\"\n",
    "    trials = trial_events(marker_data, marker_ts)\n",
    "    if not trials or not any(t['success'] for t in trials):\n",
    "        return None   # offline or no successful trials\n",
    "    mi   = [t for t in trials if t['cls'] == 'MI']\n",
    "    rest = [t for t in trials if t['cls'] == 'REST']\n",
    "\n",
    "    def _acc(ts): return sum(t['success'] for t in ts) / len(ts) if ts else None\n",
    "    def _lat(ts):\n",
    "        lats = [t['latency'] for t in ts if t['success']]\n",
    "        return float(np.mean(lats)) if lats else None\n",
    "\n",
    "    n_correct = sum(t['success'] for t in trials)\n",
    "    return dict(mk_acc = n_correct / len(trials),\n",
    "                mi_acc = _acc(mi),  rest_acc = _acc(rest),\n",
    "                mi_latency = _lat(mi), rest_latency = _lat(rest),\n",
    "                trials = trials)\n",
    "\n",
    "\n",
    "def load_session_epochs(filepath):\n",
    "    \"\"\"Preprocessing pipeline: notch → CAR → bandpass → epoch → PTP-reject.\n",
    "    Returns X, y, ch_names, sfreq, n_rejected, stats\n",
    "    where `stats` is the marker_stats() dict (None for offline sessions).\n",
    "    \"\"\"\n",
    "    eeg, eeg_ts, mk, mk_ts, ch_names, sfreq = load_xdf_file(filepath)\n",
    "\n",
    "    stats = marker_stats(mk, mk_ts)\n",
    "\n",
    "    eeg = notch_filter(eeg, NOTCH_FREQ, sfreq)\n",
    "    if USE_CAR:\n",
    "        eeg = car(eeg)\n",
    "    eeg_bp = bandpass(eeg, BP_LO, BP_HI, sfreq)\n",
    "\n",
    "    mi   = extract_epochs(eeg_bp, eeg_ts, mk, mk_ts, sfreq, MI_BEGIN)\n",
    "    rest = extract_epochs(eeg_bp, eeg_ts, mk, mk_ts, sfreq, REST_BEGIN)\n",
    "\n",
    "    n_pre = int(abs(T_PRE) * sfreq)\n",
    "    if mi.shape[-1]   > n_pre: mi   = mi[..., n_pre:]\n",
    "    if rest.shape[-1] > n_pre: rest = rest[..., n_pre:]\n",
    "\n",
    "    n = min(mi.shape[-1], rest.shape[-1]) if (mi.size and rest.size) else 0\n",
    "    mi, rest = mi[..., :n], rest[..., :n]\n",
    "\n",
    "    n0_mi, n0_rest = len(mi), len(rest)\n",
    "    mi   = mi[reject_by_ptp(mi)]\n",
    "    rest = rest[reject_by_ptp(rest)]\n",
    "    n_rejected = (n0_mi - len(mi)) + (n0_rest - len(rest))\n",
    "\n",
    "    X = np.concatenate([mi, rest], axis=0) if (len(mi) or len(rest)) else np.empty((0, len(ch_names), 0))\n",
    "    y = np.concatenate([np.ones(len(mi), int), np.zeros(len(rest), int)])\n",
    "    return X, y, ch_names, sfreq, n_rejected, stats\n",
    "\n",
    "\n",
    "# ── CSP + LDA (2-class, numpy/scipy only) ────────────────────────────────────\n",
    "\n",
    "def _mean_cov(X):\n",
    "    covs = np.einsum('ijk,ilk->ijl', X, X)\n",
    "    covs /= np.trace(covs, axis1=1, axis2=2)[:, None, None]\n",
    "    return covs.mean(axis=0)\n",
    "\n",
    "\n",
    "class CSPLDA:\n",
    "    \"\"\"CSP log-var features + LDA. Ramoser 2000; Blankertz 2008.\n",
    "    Ledoit-Wolf shrinkage keeps the generalized eigenproblem well-posed after CAR.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, n_csp=N_CSP, cov_shrink=0.05, lda_reg=1e-4):\n",
    "        self.n_csp      = n_csp\n",
    "        self.cov_shrink = cov_shrink\n",
    "        self.lda_reg    = lda_reg\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        assert set(np.unique(y)) == {0, 1}\n",
    "        C1 = _mean_cov(X[y == 1])\n",
    "        C0 = _mean_cov(X[y == 0])\n",
    "        n_ch = C1.shape[0]\n",
    "        s = self.cov_shrink\n",
    "        C1 = (1 - s) * C1 + s * (np.trace(C1) / n_ch) * np.eye(n_ch)\n",
    "        C0 = (1 - s) * C0 + s * (np.trace(C0) / n_ch) * np.eye(n_ch)\n",
    "        evals, evecs = eigh(C1, C0 + C1)\n",
    "        order  = np.argsort(evals)\n",
    "        k      = self.n_csp // 2\n",
    "        self.filters_ = np.concatenate([evecs[:, order[:k]],\n",
    "                                        evecs[:, order[-k:]]], axis=1).T\n",
    "        F = self._features(X)\n",
    "        mu1, mu0 = F[y == 1].mean(0), F[y == 0].mean(0)\n",
    "        Sw = np.cov(F[y == 1].T, ddof=1) + np.cov(F[y == 0].T, ddof=1)\n",
    "        Sw += self.lda_reg * np.eye(Sw.shape[0])\n",
    "        self.coef_      = np.linalg.solve(Sw, mu1 - mu0)\n",
    "        self.intercept_ = -self.coef_ @ ((mu1 + mu0) / 2)\n",
    "        return self\n",
    "\n",
    "    def _features(self, X):\n",
    "        Z   = np.einsum('fc,ncs->nfs', self.filters_, X)\n",
    "        var = Z.var(axis=-1, ddof=1)\n",
    "        return np.log(var / var.sum(axis=1, keepdims=True))\n",
    "\n",
    "    def decision_function(self, X):\n",
    "        return self._features(X) @ self.coef_ + self.intercept_\n",
    "\n",
    "    def predict(self, X):\n",
    "        return (self.decision_function(X) > 0).astype(int)\n",
    "\n",
    "\n",
    "# ── Evaluation metrics ───────────────────────────────────────────────────────\n",
    "\n",
    "def evaluate(clf, X, y):\n",
    "    margin = clf.decision_function(X)\n",
    "    pred   = (margin > 0).astype(int)\n",
    "    amp    = np.abs(margin).mean()\n",
    "    m1, m0 = margin[y == 1], margin[y == 0]\n",
    "    fisher = (m1.mean() - m0.mean()) ** 2 / (m1.var(ddof=1) + m0.var(ddof=1) + 1e-30)\n",
    "    return dict(amp=amp, fisher=fisher, margin=margin, y=y, pred=pred)\n",
    "\n",
    "\n",
    "def spectral_snr(X, y, ch_idx, sfreq, band=MU_BAND):\n",
    "    def band_pwr(sig):\n",
    "        f, p = welch(sig, fs=sfreq,\n",
    "                     nperseg=min(int(sfreq * 2), sig.shape[-1]),\n",
    "                     noverlap=int(sfreq), axis=-1)\n",
    "        m = (f >= band[0]) & (f < band[1])\n",
    "        return np.trapezoid(p[..., m], f[m], axis=-1).mean()\n",
    "    return band_pwr(X[y == 0][:, ch_idx, :]) / (band_pwr(X[y == 1][:, ch_idx, :]) + 1e-30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98d225db",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d266216b",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 24 XDF file(s).\n",
      "Preprocessing: notch 60 Hz → CAR → bandpass 8–30 Hz → baseline-correct → PTP-reject @ 100 µV\n",
      "\n",
      "  002/S001  OFFLINE FES    n= 85  (MI=43, REST=42)  rej=5\n",
      "  002/S002  ONLINE  FES    n= 53  (MI=27, REST=26)  rej=7  acc=0.883 (MI=0.87, REST=0.90)  lat(MI=2.74s, REST=2.46s)\n",
      "  002/S003  ONLINE  NOFES  n= 52  (MI=26, REST=26)  rej=8  acc=0.833 (MI=0.87, REST=0.80)  lat(MI=2.26s, REST=2.29s)\n",
      "  002/S004  OFFLINE NOFES  n= 90  (MI=45, REST=45)  rej=0\n",
      "  002/S005  ONLINE  NOFES  n= 60  (MI=30, REST=30)  rej=0  acc=0.850 (MI=0.83, REST=0.87)  lat(MI=2.33s, REST=2.15s)\n",
      "  002/S006  ONLINE  FES    n= 56  (MI=27, REST=29)  rej=4  acc=0.917 (MI=1.00, REST=0.83)  lat(MI=2.26s, REST=1.76s)\n",
      "  003/S001  OFFLINE FES    n= 89  (MI=44, REST=45)  rej=1\n",
      "  003/S002  ONLINE  FES    n= 59  (MI=29, REST=30)  rej=1  acc=0.750 (MI=0.70, REST=0.80)  lat(MI=2.59s, REST=2.12s)\n",
      "  003/S003  ONLINE  NOFES  n= 38  (MI=17, REST=21)  rej=0  acc=0.763 (MI=0.76, REST=0.76)  lat(MI=2.15s, REST=2.74s)\n",
      "  003/S004  OFFLINE NOFES  n= 86  (MI=42, REST=44)  rej=4\n",
      "  003/S005  ONLINE  NOFES  n= 43  (MI=19, REST=24)  rej=17  acc=0.717 (MI=0.67, REST=0.77)  lat(MI=2.58s, REST=2.53s)\n",
      "  003/S006  ONLINE  FES    n= 52  (MI=23, REST=29)  rej=8  acc=0.767 (MI=0.77, REST=0.77)  lat(MI=2.31s, REST=2.56s)\n",
      "  005/S001  OFFLINE FES    n= 90  (MI=45, REST=45)  rej=0\n",
      "  005/S002  ONLINE  FES    n= 60  (MI=30, REST=30)  rej=0  acc=0.800 (MI=0.67, REST=0.93)  lat(MI=2.77s, REST=2.13s)\n",
      "  005/S003  ONLINE  NOFES  n= 59  (MI=29, REST=30)  rej=1  acc=0.933 (MI=0.97, REST=0.90)  lat(MI=1.86s, REST=2.23s)\n",
      "  005/S004  OFFLINE NOFES  n= 89  (MI=44, REST=45)  rej=1\n",
      "  005/S005  ONLINE  NOFES  n= 58  (MI=28, REST=30)  rej=2  acc=0.783 (MI=0.67, REST=0.90)  lat(MI=2.12s, REST=2.73s)\n",
      "  005/S006  ONLINE  FES    n= 59  (MI=30, REST=29)  rej=1  acc=0.917 (MI=0.90, REST=0.93)  lat(MI=2.31s, REST=2.27s)\n",
      "  009/S001  OFFLINE FES    n= 57  (MI=33, REST=24)  rej=33\n",
      "  009/S002  ONLINE  FES    n= 42  (MI=21, REST=21)  rej=18  acc=0.717 (MI=0.80, REST=0.63)  lat(MI=2.56s, REST=2.09s)\n",
      "  009/S003  ONLINE  NOFES  n=  1  (MI=1, REST=0)  rej=59  acc=0.717 (MI=0.63, REST=0.80)  lat(MI=2.36s, REST=2.47s)\n",
      "  009/S004  OFFLINE NOFES  n= 86  (MI=42, REST=44)  rej=4\n",
      "  009/S005  ONLINE  NOFES  n= 60  (MI=30, REST=30)  rej=0  acc=0.850 (MI=0.80, REST=0.90)  lat(MI=2.22s, REST=2.00s)\n",
      "  009/S006  ONLINE  FES    n= 50  (MI=26, REST=24)  rej=10  acc=0.817 (MI=0.83, REST=0.80)  lat(MI=2.64s, REST=1.69s)\n",
      "\n",
      "Loaded 4 subject(s): ['002', '003', '005', '009']  |  total artifact-rejected epochs: 184\n"
     ]
    }
   ],
   "source": [
    "xdf_files = sorted(glob.glob(os.path.join(DATA_DIR, '*.xdf')))\n",
    "print(f'Found {len(xdf_files)} XDF file(s).')\n",
    "print(f'Preprocessing: notch {NOTCH_FREQ:.0f} Hz → '\n",
    "      f'{\"CAR → \" if USE_CAR else \"\"}bandpass {BP_LO:.0f}–{BP_HI:.0f} Hz → '\n",
    "      f'baseline-correct → PTP-reject @ {PTP_REJECT_UV:.0f} µV\\n')\n",
    "\n",
    "sessions  = {}\n",
    "total_rej = 0\n",
    "\n",
    "for fp in xdf_files:\n",
    "    meta = parse_session(fp)\n",
    "    if meta is None:\n",
    "        print(f'  SKIP (unparsed): {os.path.basename(fp)}')\n",
    "        continue\n",
    "    subj, ses_id, kind, stim = meta\n",
    "    try:\n",
    "        X, y, ch_names, sfreq, n_rej, stats = load_session_epochs(fp)\n",
    "    except Exception as e:\n",
    "        print(f'  ERROR {os.path.basename(fp)}: {e}')\n",
    "        continue\n",
    "\n",
    "    sessions.setdefault(subj, {})[ses_id] = dict(\n",
    "        X=X, y=y, kind=kind, stim=stim,\n",
    "        ch_names=ch_names, sfreq=sfreq,\n",
    "        stats=stats, file=os.path.basename(fp))\n",
    "    total_rej += n_rej\n",
    "\n",
    "    if stats is not None:\n",
    "        info = (f'  acc={stats[\"mk_acc\"]:.3f} '\n",
    "                f'(MI={stats[\"mi_acc\"]:.2f}, REST={stats[\"rest_acc\"]:.2f})  '\n",
    "                f'lat(MI={stats[\"mi_latency\"]:.2f}s, REST={stats[\"rest_latency\"]:.2f}s)'\n",
    "                if stats['mi_acc'] is not None and stats['rest_acc'] is not None else '')\n",
    "    else:\n",
    "        info = ''\n",
    "    print(f'  {subj}/{ses_id}  {kind:<7} {stim:<5}  '\n",
    "          f'n={len(y):3d}  (MI={int(y.sum())}, REST={int((1-y).sum())})  '\n",
    "          f'rej={n_rej}{info}')\n",
    "\n",
    "subjects = sorted(sessions.keys())\n",
    "print(f'\\nLoaded {len(subjects)} subject(s): {subjects}  |  '\n",
    "      f'total artifact-rejected epochs: {total_rej}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b8c8bea",
   "metadata": {},
   "source": [
    "## Verify Session Layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "611baf23",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T19:27:24.182586Z",
     "iopub.status.busy": "2026-04-22T19:27:24.182447Z",
     "iopub.status.idle": "2026-04-22T19:27:24.188463Z",
     "shell.execute_reply": "2026-04-22T19:27:24.188025Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channels (32): ['Fp1', 'Fpz', 'Fp2', 'F7', 'F3', 'Fz', 'F4', 'F8', 'FC5', 'FC1', 'FC2', 'FC6', 'M1', 'T7', 'C3', 'Cz', 'C4', 'T8', 'M2', 'CP5', 'CP1', 'CP2', 'CP6', 'P7', 'P3', 'Pz', 'P4', 'P8', 'POz', 'O1', 'Oz', 'O2']\n",
      "Sampling rate: 512.0 Hz\n",
      "Motor channels ['C3', 'Cz', 'C4'] → indices [14, 15, 16]\n"
     ]
    }
   ],
   "source": [
    "# Verify channel layout is consistent across sessions, locate motor channels\n",
    "ref_subj = subjects[0]\n",
    "ref_ses  = next(iter(sessions[ref_subj].values()))\n",
    "channel_names_global = ref_ses['ch_names']\n",
    "sfreq_global         = ref_ses['sfreq']\n",
    "\n",
    "mismatches = [f'{subj}/{sid}' for subj in subjects for sid, s in sessions[subj].items()\n",
    "              if s['ch_names'] != channel_names_global]\n",
    "if mismatches:\n",
    "    print('!! channel mismatch in:', mismatches)\n",
    "\n",
    "motor_idx_global = [channel_names_global.index(c) for c in MOTOR_CH\n",
    "                    if c in channel_names_global]\n",
    "\n",
    "print(f'Channels ({len(channel_names_global)}): {channel_names_global}')\n",
    "print(f'Sampling rate: {sfreq_global} Hz')\n",
    "print(f'Motor channels {MOTOR_CH} → indices {motor_idx_global}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70922abb",
   "metadata": {},
   "source": [
    "## Train CSP + LDA on OFFLINE, Evaluate on ONLINE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f5e80da3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T19:27:24.190995Z",
     "iopub.status.busy": "2026-04-22T19:27:24.190909Z",
     "iopub.status.idle": "2026-04-22T19:27:26.129399Z",
     "shell.execute_reply": "2026-04-22T19:27:26.128933Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[009] Pair1 (train=OFFLINE_FES) / NOFES: only 1 clean epochs — acc=0.717, skipping EEG-derived metrics\n",
      "\n",
      "Subj  Pair                         Cond      n  trainAcc   mkAcc  MIacc  REacc  MIlat  RElat   |marg|   Fisher   muSNR\n",
      "----------------------------------------------------------------------------------------------------------------------\n",
      "002   Pair1 (train=OFFLINE_FES)    FES      53     0.659   0.883   0.87   0.90   2.74   2.46    0.778    0.288   1.197\n",
      "002   Pair1 (train=OFFLINE_FES)    NOFES    52     0.659   0.833   0.87   0.80   2.26   2.29    1.162    0.073   1.452\n",
      "002   Pair2 (train=OFFLINE_NOFES)  FES      56     0.811   0.917   1.00   0.83   2.26   1.76    1.012    3.133   1.665\n",
      "002   Pair2 (train=OFFLINE_NOFES)  NOFES    60     0.811   0.850   0.83   0.87   2.33   2.15    0.756    0.695   1.576\n",
      "003   Pair1 (train=OFFLINE_FES)    FES      59     0.843   0.750   0.70   0.80   2.59   2.12    0.879    0.017   1.347\n",
      "003   Pair1 (train=OFFLINE_FES)    NOFES    38     0.843   0.763   0.76   0.76   2.15   2.74    0.910    0.683   1.219\n",
      "003   Pair2 (train=OFFLINE_NOFES)  FES      52     0.907   0.767   0.77   0.77   2.31   2.56    1.166    0.000   1.273\n",
      "003   Pair2 (train=OFFLINE_NOFES)  NOFES    43     0.907   0.717   0.67   0.77   2.58   2.53    1.071    0.029   1.296\n",
      "005   Pair1 (train=OFFLINE_FES)    FES      60     0.978   0.800   0.67   0.93   2.77   2.13    3.346    2.498   2.695\n",
      "005   Pair1 (train=OFFLINE_FES)    NOFES    59     0.978   0.933   0.97   0.90   1.86   2.23    3.620    3.029   2.399\n",
      "005   Pair2 (train=OFFLINE_NOFES)  FES      59     1.000   0.917   0.90   0.93   2.31   2.27    5.740    6.291   2.911\n",
      "005   Pair2 (train=OFFLINE_NOFES)  NOFES    58     1.000   0.783   0.67   0.90   2.12   2.73    5.261    4.325   2.287\n",
      "009   Pair1 (train=OFFLINE_FES)    FES      42     0.667   0.717   0.80   0.63   2.56   2.09    0.304    0.040   1.326\n",
      "009   Pair1 (train=OFFLINE_FES)    NOFES     1     0.667   0.717   0.63   0.80   2.36   2.47       --       --      --\n",
      "009   Pair2 (train=OFFLINE_NOFES)  FES      50     1.000   0.817   0.83   0.80   2.64   1.69    4.673    6.192   1.594\n",
      "009   Pair2 (train=OFFLINE_NOFES)  NOFES    60     1.000   0.850   0.80   0.90   2.22   2.00    3.754    8.959   2.000\n"
     ]
    }
   ],
   "source": [
    "MIN_TEST_TRIALS = 10\n",
    "\n",
    "results = []\n",
    "\n",
    "for subj in subjects:\n",
    "    subj_ses = sessions[subj]\n",
    "\n",
    "    for pair in PAIRS:\n",
    "        needed  = (pair['train'], pair['online_fes'], pair['online_nofes'])\n",
    "        missing = [k for k in needed if k not in subj_ses]\n",
    "        if missing:\n",
    "            print(f'[{subj}] {pair[\"name\"]}: missing {missing} — skipping')\n",
    "            continue\n",
    "\n",
    "        train = subj_ses[pair['train']]\n",
    "        if set(np.unique(train['y'])) != {0, 1}:\n",
    "            print(f'[{subj}] {pair[\"name\"]}: training set lacks both classes — skipping')\n",
    "            continue\n",
    "\n",
    "        clf       = CSPLDA(n_csp=N_CSP).fit(train['X'], train['y'])\n",
    "        train_acc = (clf.predict(train['X']) == train['y']).mean()\n",
    "\n",
    "        for cond_key, cond_label in [('online_fes', 'FES'), ('online_nofes', 'NOFES')]:\n",
    "            te = subj_ses[pair[cond_key]]\n",
    "            st = te['stats']\n",
    "            if st is None:\n",
    "                print(f'[{subj}] {pair[\"name\"]} / {cond_label}: no EARLYSTOP markers — skipping')\n",
    "                continue\n",
    "\n",
    "            row = dict(\n",
    "                subject=subj, pair=pair['name'], condition=cond_label,\n",
    "                train_file=train['file'], test_file=te['file'],\n",
    "                train_acc=train_acc, n_test=len(te['y']),\n",
    "                acc          = st['mk_acc'],\n",
    "                mi_acc       = st['mi_acc'],\n",
    "                rest_acc     = st['rest_acc'],\n",
    "                mi_latency   = st['mi_latency'],\n",
    "                rest_latency = st['rest_latency'],\n",
    "                trials       = st['trials'],   # ordered per-trial records for trajectory analysis\n",
    "            )\n",
    "\n",
    "            # EEG-based metrics require enough clean epochs of both classes\n",
    "            if len(te['y']) >= MIN_TEST_TRIALS and set(np.unique(te['y'])) == {0, 1}:\n",
    "                res   = evaluate(clf, te['X'], te['y'])\n",
    "                snr_s = spectral_snr(te['X'], te['y'], motor_idx_global, te['sfreq'])\n",
    "                row.update(amp=res['amp'], fisher=res['fisher'], mu_snr=snr_s,\n",
    "                           margin=res['margin'], y_test=res['y'], pred=res['pred'])\n",
    "            else:\n",
    "                print(f'[{subj}] {pair[\"name\"]} / {cond_label}: only {len(te[\"y\"])} clean epochs — '\n",
    "                      f'acc={st[\"mk_acc\"]:.3f}, skipping EEG-derived metrics')\n",
    "                row.update(amp=np.nan, fisher=np.nan, mu_snr=np.nan,\n",
    "                           margin=np.array([]), y_test=np.array([]), pred=np.array([]))\n",
    "\n",
    "            results.append(row)\n",
    "\n",
    "hdr = (f'{\"Subj\":<5} {\"Pair\":<28} {\"Cond\":<6} {\"n\":>4} '\n",
    "       f'{\"trainAcc\":>9} {\"mkAcc\":>7} {\"MIacc\":>6} {\"REacc\":>6} '\n",
    "       f'{\"MIlat\":>6} {\"RElat\":>6} {\"|marg|\":>8} {\"Fisher\":>8} {\"muSNR\":>7}')\n",
    "print('\\n' + hdr)\n",
    "print('-' * len(hdr))\n",
    "for r in results:\n",
    "    fmt = lambda v, s: (f'{v:{s}}' if (v is not None and not (isinstance(v, float) and np.isnan(v))) else f'{\"--\":>{int(s.split(\".\")[0].lstrip(\">\"))}}')\n",
    "    print(f'{r[\"subject\"]:<5} {r[\"pair\"]:<28} {r[\"condition\"]:<6} {r[\"n_test\"]:>4} '\n",
    "          f'{r[\"train_acc\"]:>9.3f} {r[\"acc\"]:>7.3f} '\n",
    "          f'{r[\"mi_acc\"]:>6.2f} {r[\"rest_acc\"]:>6.2f} '\n",
    "          f'{r[\"mi_latency\"]:>6.2f} {r[\"rest_latency\"]:>6.2f} '\n",
    "          f'{fmt(r[\"amp\"], \">8.3f\")} {fmt(r[\"fisher\"], \">8.3f\")} {fmt(r[\"mu_snr\"], \">7.3f\")}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ab81600",
   "metadata": {},
   "source": [
    "---\n",
    "## Figure 1 — Per-metric comparison (FES vs NOFES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d53e63b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T19:27:26.130998Z",
     "iopub.status.busy": "2026-04-22T19:27:26.130913Z",
     "iopub.status.idle": "2026-04-22T19:27:26.627725Z",
     "shell.execute_reply": "2026-04-22T19:27:26.627293Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1680x1080 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: fes_vs_nofes_metrics.png\n"
     ]
    }
   ],
   "source": [
    "METRICS = [\n",
    "    ('acc',    'Classification accuracy',                            '0–1'),\n",
    "    ('amp',    'Classification amplitude (mean |decision fn|)',      'a.u.'),\n",
    "    ('fisher', 'Fisher ratio on LDA projection (test-set SNR)',      'a.u.'),\n",
    "    ('mu_snr', 'μ-band power ratio  REST / MI  @ C3/Cz/C4',          'ratio'),\n",
    "]\n",
    "\n",
    "cond_color = {'FES': '#E05C2A', 'NOFES': '#2A7BE0'}\n",
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize=(14, 9))\n",
    "fig.suptitle('Online decoding: FES vs NOFES feedback  (per subject × offline-trained model)',\n",
    "             fontsize=13, fontweight='bold', y=1.00)\n",
    "\n",
    "for ax, (key, title, unit) in zip(axes.ravel(), METRICS):\n",
    "    labels, vals, colors = [], [], []\n",
    "    for subj in subjects:\n",
    "        for pair in PAIRS:\n",
    "            tag = pair['name'].split()[0]   # \"Pair1\" / \"Pair2\"\n",
    "            for cond in ('FES', 'NOFES'):\n",
    "                row = next((r for r in results\n",
    "                            if r['subject']==subj and r['pair']==pair['name']\n",
    "                            and r['condition']==cond), None)\n",
    "                if row is None: continue\n",
    "                labels.append(f'{subj}\\n{tag}\\n{cond}')\n",
    "                vals.append(row[key])\n",
    "                colors.append(cond_color[cond])\n",
    "    x = np.arange(len(vals))\n",
    "    ax.bar(x, vals, color=colors, edgecolor='white', zorder=2)\n",
    "    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=7.5)\n",
    "    ax.set_title(f'{title}  ({unit})', fontsize=11, fontweight='bold')\n",
    "    ax.grid(axis='y', alpha=0.3)\n",
    "    ax.spines[['top','right']].set_visible(False)\n",
    "    if key == 'acc':\n",
    "        ax.axhline(0.5, color='gray', linestyle='--', lw=0.8, alpha=0.6)\n",
    "\n",
    "fig.legend(handles=[Patch(color=cond_color['FES'],   label='ONLINE_FES'),\n",
    "                    Patch(color=cond_color['NOFES'], label='ONLINE_NOFES')],\n",
    "           loc='upper right', ncol=2, bbox_to_anchor=(0.98, 1.0))\n",
    "plt.tight_layout()\n",
    "plt.savefig('fes_vs_nofes_metrics.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()\n",
    "print('Saved: fes_vs_nofes_metrics.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "248740bd",
   "metadata": {},
   "source": [
    "---\n",
    "## Figure 2 — LDA decision-function distributions\n",
    "\n",
    "Visualizes classification amplitude and separability directly: wider FES vs NOFES spread between MI and REST curves = higher Fisher ratio and larger mean |margin|."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "393042a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T19:27:26.629009Z",
     "iopub.status.busy": "2026-04-22T19:27:26.628916Z",
     "iopub.status.idle": "2026-04-22T19:27:27.543034Z",
     "shell.execute_reply": "2026-04-22T19:27:27.542546Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/numpy/lib/_histograms_impl.py:897: RuntimeWarning: invalid value encountered in divide\n",
      "  return n / db / n.sum(), bin_edges\n"
     ]
    },
    {
     "data": {
      "image/png": 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hA+QzzjhDrrjiCunr65NHH31UOjs75Q1veENRX7cXvvCF87pGAAAAoFI630Npu4rJ9DINkB966KFZA2QNCrV1xSGHHCLvfe975Rvf+IbvNeiZfTt27Ci6bMOGDb5vj/JpeJx68mGxwpHiy7MZib34JXPW89iKxsVq7SQ8BgAACydA1lPutOpYh4loeKwb4FxFxuDgoPznf/6nXHrppfO9TAAAAKAimzdvNp+1hcVkucty15nJ9ddfb8Lo//mf/xHbDtbJTm+rlcuYHxoeR1btXXRZZsvTczowz27tEqHwGAAALKQAORqNyo033mg+JtMgube3V5qbmwPdp1YzX3bZZWZTrZtwHVKycuVKeeUrX2kuf9nLXjaHPwEAAAAwu7GxMfM5FotN+VpuEF7uOtPRwovLL7/c9E4+6KCDAh/2Cy+8UE499dQpFcgnnXRS4PtCfQzMKwyPTduKrc+IJ41tNFU8JwcAACzyAHkmWlHR0dER+HZaufz444/Lsccea3ost7S0yLPPPiu33HKLCZF/9KMfFbXJAAAAAKotVxSRSk3tg5tMJouuM53zzz9fVqxYIR//+MfLWsPy5cvNBxYGd2iHSEtH4wzM82Fo3JG+EXe+lwEAwILU8AGyVj7oMJD+/n5zGtZkZ511lu/7etGLXiR/+MMfplz+nve8xwwpueqqqwiQAQAAUFOFbSr2228/3+0tcr7//e/Lz372M/nqV79a1OpC+xqrbdu2mT31qlWrAp/Bh/njZbNl39ZNjkl42Wrf4bEzPir1Hh5vHXQkFJrvlQAAsDA1bICsQzzOPvts+fnPf27+Xio81s1QkAB5Olqt0dTUZCqUAWC+Jq/PxaR1AEDjOeyww+QrX/mK3H///fL617++6Gt6mTr00EOnvf1zzz2Xr0IuRdta6MdPf/pTOf744+d07agOL5OW7M5eickhZd3ejjf7D48TQ+IO9Um9h8fhkEhPe8O+vAUAoK417G/Y//f//p8Jj8877zyzke7u7p6z+85kMjI0NCTZbNb0i/v85z8viURC3vSmN83Z9wAAPzQ8TvzuhxLq6Jamg4/koAHAIqR9hi+66CIz++Piiy/OD47Wfeodd9whRx99tKxZsybfC1kv13ZuOstD6R62VIXyr3/9a7nuuuvMvvqoo46Sl770pTX+yVBueJzZtlHELb/fr92xzHd47PRvFbFDdR8er10Skc0D5VdlAwCABRgg62l45557rtxwww1zft/33XefHHPMMfm/6wb8Qx/60Kw94/Q0QK2MLqSnAwJAJULdPRJqauEgAsAi1dXVJVdffbVccMEFcuSRR5pKYu2HfO2115oQ8Jprrslf98EHHzT7WD1TT+d45Nq06cdkWiCRq14+5ZRTavgToeLw2MmK3bG07Pvx0/I4Hx6HwhJe0iP1ODAvnfXy4XE03Ph9nAEAqFcNGyBrdfDLX/7yqtz3IYccIr/4xS/Mxlz7K3/zm9+UkZER8/dwePpDdv3118u6deuqsiYAixfhMQBAQ2M9406D5Msuu0yi0aipGr7yyivl4IMP5gAtsvA41L1SrOH+qn2vwvA4smKtZHfs7p0dxBO9aUllp7YajIUt2acnWvbtn9qekSe3ZeTgNbGi8HjbcFbiEavs7wcAABZYgKy94B5//PGqVXkce+yx5s9///d/L+94xztMqPz000+b3nDTufDCC+XUU0+dUoGspx0CQKVG7r1DYnsfxIEEgEVKq4RnqxTWdhalZoOUontc/UBjDMwrDI9DrR3iVilAnhweW5Hyg1cNfx/emJJIaHeom3E8ecnaWNm318rjR59Pm8smVx47rsj/9aYkmfHK+n4AAGCBBcif+cxn5I1vfKOcdtpp8spXvrKq30sD5RNOOMH0iHv22Wdlzz33LHm95cuXmw8AAAAAmCs6MC/ctTwfHleLmxybs/A4R4PevZdF8n9/ekem7Ntrz2NtWxEJW9LdYpdsWzH5+2kVMwAAWKQBsvY+1mEh2gfuVa96ley9994SChUPd9CecDfffPOcfL/x8XHzeWBgYNoAGQAAAADmnOtUPTx2kuPijg7NaXhcks8K+ZkG5nW3hMzn2WjY3DuclUOECmQAABZlgJwbCpIbeqcfkwUNkLdt2yYrVqyYcrlWHf/gBz8ww/T222+/ClYNAAAAAMHowLyqhseJIfFGB8Wy7KqGx9peZUfCrSg81rYVvUOO3tus4fHGnRlx9KoAAGBxBsiuG3zjMZt//dd/NcPztDWGVhlrAP3YY4/JN77xDTOl+tZbb5V4PD7n3xcAAAAA5mOgbr7nsWWL3d5d1fB4y6AjY6lgr+O057GGwbnwuFTbiunC46wjsrTNrmDVAACgoQPkanjTm94kzz//vHzve9+T7du3SzablZUrV5rLL7roIjO4DwAAAAAWgsKBeaH2bpFwuKrh8UjSleZYsEC3b8SVNd227/A46+wOj1d2hqQ/MfttAADAAg+QR0dH5f777zftJ4499tiSLSj80tvrBwAAAAAslvBY21Y4O3tnaQpRpoLwuC1uS9RH7+JCOuYmSHjcn3Clp2MiPO5o0gB57s9cBQBgsWno83n+/d//XVavXi3HHXecnHXWWfLoo4+ay7V6OBaLyY033jjfSwSA+Zw3AwAAMIUzPloUHldtYJ6I6XmcC49XdYZ0UE2g2/e0h323regfdcT1vHx4DAAAFnmAfPfdd8sFF1wgr371q+Wmm24yp0XlLF++XI4//nhzHQBodPq/N3dox3wvAwAALJA3l92hvkDhsZfNlv29tOdxLjzWGTNBhUP+ex7rmJyOZpvwGACAOdawAfJnP/tZec1rXmNC4hNPPHHK11/xilfI+vXr52VtADCXNDx2k2McVAAAYGjxTEVvLtsh/+FxJi3Znb1lfyvteVxueOxH4cC8zmZLmqMN+xIXAIC61bC/XR955BF5y1veMu3XV61aZfoiA0Cj0/DYjjfP9zIAAECdhMdO35aK3lwOL+nxHR5ntm0UcZ2yv9eyVrsm4bG2rWgK2mAZAAAs7AB5Nr29vdLU1DTfywCAiml4bHcs40gCALDI5cPjsZGK3ly2wmH/4bGTFbtjadnfK2jPY790YF5heEzPYwAAqqdhA+QDDjhAfv3rX0/79R/84Afy0pe+tKZrAoBq0PC4Sq+9AABAI4bHzW1VfXO5MDwOda+UUFOL1BMNj/sTLuExAAA10rAB8jnnnGNC4i996UuSyWTMZXpq1MDAgJx77rnypz/9Sc4777z5XiYAVIzwGACAxU0H5hWGx6Glq6q2P5gSHrd2SD3RthX9o464nkflMQAANTL7uUt1SkPi++67Ty6++GL5yEc+YsLjk08+WYaGhsy78/r1008/fb6XCQAAAAAVMQPzWjoKwuPqpMdeNlv34bG2rXBdkY5m21/bCk3fAQDA4gyQ1S233CJvfvOb5Zvf/KY8/vjj4rquvPrVr5azzjprxgF7AAAAANAodGBeeNnqqofH7nC/SHdPXYfH2vO4s9mSpujsJ9NqYdGOhFuT9QEAsJA1dICsTjzxRPMBAAAAAAuRDsyranicSYsz3C+e51Y9PB5NORWFxzowry+hlcferOHxlkFHxlIEyAAALMoeyIlEQq666io56qijZPny5RKLxcxnrT6++uqrZXR0dL6XCAAAAABzOFC3euGxaVvhuWK3dFQ1PB4ad6RvxA08MK8wPPbTtiIXHo8kXWmONeRLXgAA6krDVSBrq4rjjz9eNm3aZDYGbW1tJjweHh6W3//+9+bjhhtukJ/97Geyzz77zPdyAQAAAKAitRiYZ7V0ih1vqmp4vHXQkZCPtsWFeoezsrwtXFZ43Ba3JRrw+wEAgKka6u3YTCZjBuU9//zz8sEPflCeeuopMzRPw2T9rH/Xy/Xvej29PgAAAABg+vDYtK2oQXgcDon0tAerYXICVB7rwLyBMS8fHq/qDFUvfQcAYBFpqAD5e9/7njz22GNy8803y2c+8xnZa6+9ir6uf9fWFjfeeKM8+uijctddd83bWgEAAACgHunAvKLwuMo9j3Ph8dolEQmHggW6S9ts35XHA2OuJDO7w+Nqtf0AAGCxaagA+Qc/+IEcfPDBctZZZ814vXe84x3met///vdrtjYAqCZnnN7uAABgbmR39gYKj72Z59XNSHse58LjaDh4oNsS89+2IpnxJB4hPAYAYFEHyI888oj83d/9na/r6vUefvjhqq8JAGoRHrtDfRxoAABQtD8om+sECI89cYd2lP2tQhWEx34U9jyORyzparaoPAYAYDEP0evt7Z3StmI6ej29PgA0OhMe20yAAQAAE5zEUEVvLtsdS32Hx07fFnGTY2V/L+15XIvwWNtWdDXbInStAABgcVcgJxIJaW1t9XXdlpYWc30AaHh2SMJLeuZ7FQCAeaSzPQ4//HCzx+3q6pITTjhB1q9f7+u2P/3pT+XEE080BRa6l25vb5eDDjpI1q1bJ4ODg1VfO+Y+PHb6t1b05nKoqcV/eDw2Ina8uezvFbTncbnhMQPzAAConoaqQHZdN/CmAgAanYbHVrih/ncNAJhDOkD6nHPOkQMPPNAMjE4mk3LttdfKEUccIffdd58Jg2eSC5rf+c53ysqVKyWTyciDDz4on/rUp+Tb3/62/Pd//7cJptFA4XEoXNU3l4vC4+Y2kXBU6ooZmOcVhccMzAMAoHoaLpG4++675dlnn531evQ/BrBQEB4DwOI1MDAgl1xyiaxevdqExVo9rE477TTZf//95aKLLpJ77rlnxvv44Ac/aD4KXXjhheb2H/rQh+TOO++cdUg16is8jqxYK9kdm2sSHoeWrhJv6zNSL3R9A2OuJDOExwAA1ErDBch33HGH+fCDd6EBAADQyLR4Ynh42ITIufBYrV27Vk455RS59dZbZdOmTbJmzZrA973nnnuaz7SxaIyBeV4mnQ+PrUh1KoJLhcf19Joq17YimfEkHqHyGACAWmmoAPnee++d7yUAAAAANfPAAw+Yz9quYjK9TAPkhx56yFeArPNBtP2Ffv7Tn/5kqo8jkYgcd9xxVVk75o4OzLOXr6lyeCzijQzUfXisbSviEUs6my1f6xtNOTVZHwAAC1lDBcivec1r5nsJAAAAQM1s3jzRpkBbWEyWuyx3ndm8733vM4FzzgEHHCA//OEPZd999531ttu3b5cdO3YUXbZhwwZf3xdzwA5VvfLYTQyIm05WPTzOOl7FA/O6mm0RH8sbGnekbyTYHB0AANDgATIAAACwmIyNjZnPsVhsytfi8XjRdWZz2WWXydvf/nbp7+83/ZR/97vfmT/7cf3118u6desCrR1zPFC3ym0rPA2Po/GqhsfprCe9w1k5RGJBFlgUHuvAvGf6dH3erOHx1kFHQqHK1w0AwGJHgAwAAADUqebmZvM5lUpN+Zq2oyi8zmx0aJ5+qNNPP90Mz9M+yqFQSM4444wZb6tD90499dQpFcgnnXSS758F9TdQt7DnsRWNi9XaWdXweOPOjDgBO0rsSLjS0ST58NjP+nLhcTgk0tPOS97F6LpfDQa6/ntf11m1tQDAQsBvUwAAAKBOFbap2G+//Xy3t/DjLW95i7S1tclXvvKVWQPk5cuXmw8sHJMH5tmtXb7aQlQSHmcdkaVtdqDbjqVcWd0V9h0ej6XdfHi8dklENg9kK1g5AABQwX57AwAAAKiZww47zHy+//77p3wtd9mhhx5a1n07jiPpdFoGBgYqXCUajQ7MKwyPJ9pWSNV6HufC45WdIWmJBesp0RzzX3k8nnZkaMzNh8fRcP0MAQQAoJERIANAA/CyVM8AwGKkLSK0SvjGG2+U4eHh/OUbN26UO+64Q44++mhZs2ZNvhfy448/Llu3bi26j97e3pL3fcMNN5jWGK961auq/FOg3rhDOyaFx9ULWrXncS487mgK3pB4Wavtu23F4JgntmURHgMAMMdoYQEADRAeZ3eWfvEPAFjYurq65Oqrr5YLLrhAjjzySDn//PNN6HvttdeaUO2aa67JX/fBBx+UY445Rs4++2y55ZZb8pcfeOCBcsQRR8jLX/5y0+5i586d8utf/1p+8pOfyJ577ilXXHHFPP10mK83l93kmISXrfYdHjvjo2V/L6eC8NgI0PPYtkW6W2wqjwEAmGMEyABQ50x47AacOAMAWDA0NO7u7jZB8mWXXSbRaFSOOuooufLKK+Xggw+e9fYXXXSR/OIXvzAVx/39/RKLxeTFL36xfOxjH5N/+qd/MiE1GouXSZv9QUwOKev2drzZf3icGBJ3qE/KpT2Pyw6PfSgcmNfdEjKfAQDA3CJABoB65zpidywVN1l+9Q8AoLGdcsop5mMm2s5CB6NNpkGxfmDhhMeZbRsrenPZ7ljmOzx2+reK2OWnskF7HpcbHmvP494hPSZT/w0AAIDK0AMZAOqchsehppb5XgYAAKiX8NjJmv1Bufy0PM6Hx6GwhJf0SL0ZS7tF4TED8wAAqB4CZACoc4THAACgMDwOda+s6v6gMDyOrFgrVri+TlwdTzsyNOYSHgMAUCP1tRMAAAAAgAaW3vSEeJlUya9ZkZhE1+xT1sC8ovC4tUPc4f6y1pB5/inJDmyXyKq9/YXHkajUUuHaMztscfpCksm65u9WOCKjratkcMwT27KoPAYAoEYIkAs8+eST8q1vfcsMGXnqqadkZGRE9thjDzn22GPl8ssvl5UrV9bqcQEAAADQgDT8TD35sAk7iy7PZiT24peUdZ86MC/ctTwfHvtZw9hDvzDfUwfmTfQ8nvhapnejyDQVxW5ybF7D48nHLzPcItmRFsmMJ014Pr7sRbIj64ht68A821fbiqxDT2QAACpFgFzg5ptvli9/+cvyD//wD3LaaadJU1OT/PGPf5Trr79ebrvtNvnDH/4g++67b8UHHQAAAMDCpeHn5Arf5FN/Lf8OXcd3eJyj4bFWO4eWrioamJft21JyzJyTHBd3dKj64XGJQY/THb9wLCwhOyzhDkf6ntskOxO2xJs1PA6Z9hWzSWc96R3OyiESm5u1AwCwSBEgF9DJ1h/+8Iels7Mzf9l5550nhx9+uJx//vny8Y9/XL773e/Ox+MEAAAAoEFpWwh3qK+ygboBwmNzm3jzlPB4pvV5o4NiWXZVw2PP82RHYqIdRRCDQ+PSO+RIc/fEwDz9s5SMwYvD4407M+LoVQEAQEUYolfgFa94RVF4nHPGGWeYz3/5y18qO9oAAAAAFpV8T2HbR8nsNMoZmDfRtsLyvz7LFru9u6rh8ZZBR8ZSwQLkkURKtvaNSdgWWdPu+mpbkQuPs47I0jZe8gIAUCkqkH14/vnnzecVK1bMeL3t27fLjh07ii7bsGFDJY8PAAAAgAZVOJAuvKSnpt/bR3ZctL5Qe/e0vZHnKjweSbrSHPMf6LpjCekbsuQFzbas7o5I1EcGrz2Pc+Hxys6Q9Cd8HAgAADAjAmQfPvaxj5nP73znO2e8nvZKXrdunZ+7BAAAALCAFYaz2hYiu2Oz1OP6Noy3i9u1StLplHhpkXj/7peIUVvkxV3Z/N+f6E1LKlu6dUQsbMk+PSWqlwvC47a4LQOjjjy1PTPlahv7Jy5bmbTN8DzbScqGreMynO2SI1bGJTSS9BUe9ydc6emYCI87mjRALl3xnPtZEqPFrUE2p5o1fpfVsdH8Za2bU9P/fAAALAIEyLP49Kc/LXfeeaecdNJJcvbZZ8943QsvvFBOPfXUKRXIelsAqPK8GQAAUCec8VHxMunqD6Qrk5scy4fbGh7/ZaBJZHyilV90V4Ccdi05ZFlx0KuB6w//nJCejpC0xHaXA2ccT16ytvSgOu153NEkJjxe1RmSZ/oy8n+9KUlmvCkBsieeDHohGdgelwHHkr7MElnRFZNYzJHsyOxtK/pHHXE9Lx8ez0R/lj8/l5Rtg8tl3A1Lk52V7khKNqdbTHvlhLP7pXJ4Y2ranw8AgMWAAHkGX/ziF+WjH/2oHH300fKtb31r1h5iy5cvNx8AMNfhsTtU3B4HAADULx2YZy9fU+WBdOXdzkmOizs6tDvcHg5L1PZkZWwioY11TEyde3ZodwDrZXdXIWt4fNDqeNF9Pr1jakVxjvY8Xt0VNuFx7vVUJGTJ3ssiRdfbMjjxPZZEXBlJJeQF7SEJ2R0SDuk6HF89j11XpKPZnjU8zhka96Q9nJVVoXHpiY6bth+96SYtQJY94rsrkLeH5rYNxuBd183p/XW+5b0yH/z+HPO1PgDA3GGiwDS+8IUvyMUXXyyve93r5Mc//rE0N+upTABQexoea6UQAABoEHaoyuGxV9aby9q2whsdFMuyfa9PK6mzO3vzfy+sPPZDex4XhsczGUu7sjVhm4F5e6xs2xUe+x+Y19lsSbP23fBJw+3WUCYfHgMAgNIIkEu46qqr5NJLL5Xjjz9efvSjHxEeA5hXGh7bcd7EAgCgUejAvGqGx07flsBvLud7Mlu22O3d/sLjbFYy2zaKuDNXAM9kWavtKzweTzsyNOaa8Hhtd0RisXCg8FjbVjT5mbI3KdwmPAYAYHYEyCV6Hn/4wx+WN73pTfKDH/xA4vHi07MAoNY0PLY7lnHgAQBoEFY4XN3weGwk0JvLhQP9Qhoe+1hfOuNItr9XxMmK3bG0/EX7CI+Hxh0ZHPPEtixZ0+5KNGL7GphXGB77bVsxNdye/XqjqfIDdAAAFgJ6IBe47rrrTM/jFStWyFve8ha54447ig5Wa2srA/EA1JyGx5xWCQDA4lYUHje3iYSjgcNjbVvh7OzVGXEzSqWy0tuXlv27HQl1rxRruF+qRcPjrYOO2LZId4stWkQ8fUfl3eFxf8KVno7yw2PDxwZrOBuRvhG3vPsHAGCBIEAu8NBDD5nP27Ztk3e9611TDtYee+xBgAyg5giPAQBY3HRgXmF4HFq6SrytzwQOj/20rdDw+LmtI+K4EQl1dEuoNS7ubAFymRP9cuFxOKThcch89tO2on/UEdfzKguPfdDweFu6SXy0YkaVXferQY4xAMwjWlgUuOWWW8w7+9N9PPvss/P3SAFY9NzEkKSe/qukNz0hift/nP8zAABYBAN1C8JjPz2FnfHR4OFxxpsIjx1XlnbExW5unfU2+jppR8KtKDxeuyQi4dDsP1PaEdO2wnVFOpptf+FxmeF2LjwOW670tFN3BQBY3AiQAaBBeK4jqScfFmdgu6SeXm/+7GVS870sAADggw6kq2igboDw2NxmqC9QeJzOeLJ5xDLh8cqlzdLWGvMVHm8ZdGQsFSxAHku7ReFxNOwjPM64smnYNj2PO5staY7aVQu3k24oHx6/IDbmK9wGAGAhI0AGgAZihSMidsh8djPp+V4OAADwwcukJbuzt+xjpQPzgoTHEzcKBQiPHdk0YknWs0x43NnR5Ds8Hkm60hzz/7JyPO3I0JgbKDzWthob+zOSdSd6Hjdpo+QqhdsaHg9lo/nwOGrT/xgAAM7FAYAGpJVIkp1txAwAAKiH8DizbaOI61Q4UDdYFWx4SY+v8Fgro3v7MtLpWdLT7AYOj9viE4Pv/LatGBzzxLYs3+Fx1nFMWw0Nj/dodX21rSg33Na2FRoehyyP8BgAgAJUIANAg/GyGXFHh0xlEQAAaIDw2MmK3bG0pgN1rXDYX2V0f684ris9zZ50NPn4RpPC41WdIV8LzPU8tm2R7lbbd3jcN5A0bTV6OkLSPntXjSnh9rJWO9jAPMuTznCKymMAAAoQIANAA9GBOF46KZZlm8oiAABQ/+FxqHulhJpapF4ro3Vgno/CY0N7CheGx34qowsH5nW3hHz1FNa2Ff2DSXE9mWir0eLj5FnPk4ExL3C4XTgwrzOclrBV3uA9AAAWKgJkAGggZiCOZYvd3u2rsggAANSetoUoCo9bO+p3fR3dvgbm5WhP4SDh8Whqd3isbSv8hMc6ME/bVjiuJ52tEd9tNQbGXElmgq2vMDw2A/Mseh4DADAZATIANEjlsaED9GJNhMcAANQxMzCvjsNjd7g/vz67uXX22xQU5GpPYb/hrOobCTYwL+2IGZinbSs6W6PS3BT13bYimfEkHgkWbheGxwzMAwCgNAJkAGiA8NhUHucG4mjzQADAonLXXXfJ4YcfLi0tLdLV1SUnnHCCrF+/3tdt//M//1POOeccOeCAA6StrU2WLVsmr3rVq+RrX/uaZLPZqq99UXKdqofH+TeXA7atcIb7xfNc3+vTcNYd2pH/u/YUDjLQLxQgPM46nmwats3APG1bESQ81rYV8YglXc1WsHCb8BgAgFmRQgBAnTPh8a6BebStAIDF5+abb5aTTz5ZRkdH5aqrrpKPfvSj8sgjj8gRRxwhf/3rX2e9/bnnniv33nuvHH/88fJv//Zv8uEPf9gEx+9+97vlxBNPNAEc5pYOzKtqeJwYyr+5HLjnseeK3dLhOzx2+raImxwre6JfT3vYd3jcn3BNeKwD8/y2rSgcmNfVHDzcpvIYAIDZ0UATAOqdHTKVx5neZ+d7JQCAGhsYGJBLLrlEVq9eLffdd5+0t7eby0877TTZf//95aKLLpJ77rlnxvv41re+Ja997WuLgrWLL75Yjj76aPnJT34iP/3pT+WNb3xj1X+WxaSaA/M0PHb6t+bfXA460M9q6RQ7Pns4q8Pr3ME+cSMjYseby16vr57HWU/6Rx1xPU9WtrrSlA4HDo+1bcUzffq9vGDh9jbXV7gNAMBiRgUyANQ507aCgXkAsCjdfffdMjw8bFpQ5MJjtXbtWjnllFNMZfGmTZtmvI/Xve51U6oyQ6GQnHrqqebPf/nLX6q0elQtPA6Fzf4gaHhs2lb4CY9dkR07R03lsd3cJnbHMqkWDY837syY79nRbEu7n3l+ZmCeVxQeB6k8DhRuu7b0DtPqBQCwuBEgA0CdIzwGgMXrgQceMJ+1XcVkucseeuihsu77+eefN59XrFgx63W3b98ujz76aNHHhg0byvq+qDw8jqxY62t/MCU89tG2QoPc57cNy+i4YyqPQ0tXBe1aETg8zjoinc2WNEdtX5XHA2OuJDOVhce+1ufa8nyqWRynKncPAEDDoIUFAAAAUKc2b95sPmsLi8lyl+WuE/R+v/rVr5qBfNoHeTbXX3+9rFu3LvD3wdwNzNMwOB8eR3wMl8tmyw6PR0az0tIUFbtzqVjW7OnpaMqpKDxe2RmSvoS25Ji5VYS269a2FcmMJ/FIbcLjrGfL0jbqrgAAixsBMgAAAFCnxsYmhpfFYlPP64/H40XX8SuRSJjQWFtj3HnnnbJkyZJZb3PhhRfmW17kaAXySSedFOh7L2aVzCrUgXn28jW+w2OV3dkr4a7lvsNjzxXZmvAkaWWlrSUs8aYWsazZWzcMjTvSNzJ7H+GitTnF4XFHU8jn+iwZt1yJRyxTsewnPC4n3M56Vj48XhEdl2TMf79pAAAWIgJkAGhQWlkEAFjYmpsnhpelUqkpX0smk0XX8Rse68C8P//5z/LlL39Z3vzmN/u63fLly80HyqNtF9yhHeUfPjsUKDw2XMd/5bFW9iZEEmlbulrC8oIV7bJxRHyFx1sHHQmFgoXH/QlXejr8h8daGb1lICWpuCVdcVu6mm0RH4XHZYXbni2D2aisiKZMeNwezsjEvzQAABYvzsUBgAYNj7WyCACwsM3UpmKm9haljIyMyPHHHy+///3v5YYbbjBVxahNeOz0bTED6SoaqBskPNYXeh1L/VUee54ZmJfI2NIadU14bNv+w+NwSKSnPey7bUX/qCOu5/mvPJZdbTVSIm1Rz7St8NOUuZxwW9tWaHjseFY+PAYAAATIAFC3hn9xu4zce4eM/fk35iPHc11xh/sl9X//Y74OAFi4DjvsMPP5/vvvn/K13GWHHnrorPczNDQkxx13nLnNTTfdJOeff34VVotpw+OxETOQrpYDdUNNLf4qowf7zMC81ogrK1utwOHx2iURCYcs3z2PtZq4o9n2HR4PDCVNT+a2mMjKVs9X24qywu3cwDzPko5wmvAYAIACVCADQINVHnupcfE8V6xY+S9EAQCNQXsMt7W1yY033mh6Fuds3LhR7rjjDjn66KNlzZo1+V7Ijz/+uGzdurVkePzQQw/JLbfcIu9617tq/nPIYg+Pm9vE7lgm9VoZ3dIUklWt4is81p7CheFxNOw/PNaex9q7uDk6+zfSoHlgaFzG047pybyqK+an8FjG0m7wcLtgYJ6Gx3E7eN9kAAAWMnogA0ADMW0rPFfslg7xxhLzvRwAQJV1dXXJ1VdfLRdccIEceeSRpnJY+yFfe+21phLzmmuuyV/3wQcflGOOOUbOPvtsExTnHHvssfLf//3fZnCe3ua2224r+h4HH3yw+cDcDswrDI9DS1eJt/WZOq2MXiHLIi1i9fu7rfYUXtNt+w6PJw/M60to5bE368A8bVsxnmqSpljItNVwd47O+r00bB4acyW8LEC4XRAea9uK/kywViEAACwGBMgA0AC0bYWlTfxcR6xo3JwGS20MACwOGhp3d3ebIPmyyy6TaDQqRx11lFx55ZW+gl8Nj9Xdd99tPia74oorCJDnmBmY19KRD4/9tF2oZbjtjQzsroy2loq90//tdTviN5xVvcNZWd4W9t/zWAf6DaRkNBSTppgtXR1xse20uD7aVgyOeWJbVqBwuzA8pucxAAClESADQAOEx954QqxIlxmIY4Uj870kAECNnXLKKeZjJtrOQitLJyt1GapL20KEl62uanisba0C30Z7HicGxE0nd4fbO2dfnzO+u/pXewr7DY/NbXdVHvsLjz3ZmrDMwLyOZWHpamoSP98p1/NYW3B0t9iBwu02wuN5cfsfh2Vl76DUi+t+FWwt731dZ9XWAgD1iB7IAFDnNDw253L6HIgDAADml54pVNXwOJOeaGtVRtsKT8PjaNz3+pzEkLhDffm/++kpXGhpm7+BeVp6PDDmyUjaMgPztG1FkPBYex53t4QCrU/DbSqPAQCYHQEyANQ7BuYBANBQdGBeNcPjzLaNpq1VOT2PtRWW1dbla33uWEKc/q0ito8AeBotMX+VxwNjriQzrrRFPTMwz89Av8Lw2O/AvMnhtq+2FVTxAwAWOQJkAKhzVqxZrAgDXQAAaBTVanmcD4+drGlrFXhgnvY8bvUXHo8kUuIM9YuEwhJe0jMHq59+fVsGHUlmPIlHbFnZ6onl41XqWNotCo+DtNUIFm6L7EjM1oEZAICFjR7IAFDnCI8BAEBheBzqXinWcH/g8Ni0rdj6jMzWFXtwaFz6hrS/Q0giK9ZKdsfmqobHI0lX4hFLOpstX+H7eNqRoTFXwsvKD4/9rU+kN90kYyl/AfLgXddJPav39SUfe2jW68T3O7QmawEAFKMCGQAAAADqmA7MKwyPQ60ds9/Gk6nhsY90VsPjrX1jErJtCXf3+HojO+t4FYXHbXFbupptX+vTthWDY57YllWT8DjhRKQ5xstmAMDixm9CAAAAAKhjZmBegPBYuUM7AofHQ+PeRHgcsqVnabNY4dlPWE1nPekdzkol4fGqzpCvvh/DKTFtK7Q/cner7Ss8LifcVrnwuDWUkWWtvGwGACxu/CYEgAalL74AAEBjcMZHy7+x6wQKj81NkmMBw2OR3jHbhMd7rGyTaCTkKzzeuDMjjhOstHdgzCsKj31VRo9mZWvCNj2Pu1tCvgbmlRNuqyEnmg+Pe6Lj1WtqDQBAg6AHMgDUKWe4TxwvLG7uBaclkh3Yvjs8To7N7wIBAIAvTmJI3KG+so+WDswLEh6b28SbfYfHOjBveMySsOWZ8DgWC4sk/YXHWUdkaZu/uiTdvwyMuZLMBAyPh8ald8iR5u6Jnsf6Z5mlk3M54bZur4ayUUm6oXx4THYMAAABMgDULWewT1w3ImJPVAA5oyPiOVnz4ssbGRAvm5nvJQIAAB/hsdO/Nf/7vByhppbAt7E7lvkKZ92xhPQNWbLC8mR1mzcRHov/8HhlZ0j6E5bvthXJjCfxiP/weGw8LVuTYxK2Rda0u77aVpQXbk+0rdDwOB5yCI8BAChACwsAqFf6QnPSi01TeZxOiptOihWOzNvSAABAgPA4FJbwkp6aHjI/lbNmfUP9ZmDeGg2PI/56CheGxx1NoUA9j+MRS7qaLd/h8WAiY9pqrO2OSNRHBj95fS0xP+vb3fM4bjvSEUpTeQwAQAECZABoFPnK46zY0bhIvHm+VwQAAHyEx5EVa30NpJuvymgdmBf1ER4r7SkcJDyWSQPzuppt320rBhNpsS3Z1ZPZ9hUe9yfcgOF28cC8jnB69p8JAIBFhgAZABqAqTzOZnZVHofFauvy9eILAADMz8C8ovA4Eq2rh0EH7OUro7t7fA3My3GChMcisiPhFg3ME78D8/rGJGRbsrQr7rutRv+oI67nBQq3C8NjMzAPAABMQYAMAHUu17bCc3dVHkfjhMcAANQxMzCvyuGxbg/K4STHxR0dClQZrWc/5WhPYb/hsRpLBRuYN5wSMyRP21Z0d8YlHAr57nnsuiIdzXagcLswPOa9eQAASiNAnuQzn/mMnH766fLiF79YbNuWcJ2dagZgcTED8zQ8zmbFsqk8BgCgIdihKofHnrhDO8pqW+GNDopl2b7X52XSkt3Zm/+7n57ChZpjAQbmpV3ZmrDNwDxtWxEkPNa2FZ3NljRH7UDhNuExAACzIx2d5PLLL5fOzk556UtfKolEQnbsCL4xA4A5kas8drJih8PiiUflMQAADUAH5lUzPHb6tpg2FGX1PLZssdu7/YXH2axktm0UcZ2y17us1V/P4/G0I0NjrqxtEenpjvhuW1E4MK8voYGzFyjcpvIYAIDZESBPsmHDBnnhC19o/nz00UcTIAOYPxoeh62JU0u1dYWz+/RRAAAwf9KbnhAvkyr5tczzT4kzsnPK5dmBbWLp7/NpWJGYRNfs4y88HhsRLzVuvtd0a8gObJfIqr2nDPQLtXeL+DjLMp1xJNvfK9KSNd9X7zMTtcXpC0km6xZd10lERV6wpvj2m56QzJaMOP0hyTi7r+8MhsQdHZbxzKiEu5bn21bs3B4W27JkZfR5sZNDIvKC4u8xMpAPvfU+NDR+OuOJLmVlqystiYg22JDg4fbs1xtNlR+gAwCwEBAgT5ILjwFgvpm2FeEmeh4DAFBnNDxOPfmwWOGIGZhneh7bIVN5nO3bUjqgdRxJbfy/fGsq0xbCdcTuWCp2JCqxF7/Ed3hsN7eJ1dyev7/JMr0b82soDI+1bYWzs3fWGt1UKiu9fWnZv9uRUPdKcYb6zPfKNIckO9IimfGC76lvcLfuU/IYpTdtkOxYR9H1naEOcRPDksn2ipccMwPztOexm1oinS0hsf9/e/cCHlV55gH8PefMNZNkEhIgQLh5rVSw3RWKsAJad7VWkFWodRWplwpqXbveVqquUrHV2j6yWnWtpV7QXlZLS2tbfVoEbBHFbSsttl5QLokkkHsyyVzP+fZ5v2HGmWSS+U4gmUv+v+eJE8+cmflyToZ8859v3rdpP1Gm0hWWSdGDdbKkRrilXDbMq2xupRq/Qd4gkXAfR7ap1GSOOam5Kz0wBwAAGGkQIB9Fhw4d6rNimVc0AwAMRmLlscrHPgEAAGB4cXjMpSA40NTHTEzWFDbbD/Ub0PJtHKNrZVkIXn3L4axR6qfogQ+zVrVKDY+N6vGkNewhcjiTq4xTcYgtMoTHKmUrODze19BFpuUkw19FRqknZewTyNAd5PB/vCI31txAB7s1+uBQNO1+ok067Q2VUYtzLB1bHUlu18MusnoCpBkGBZwV1BTrIVe5TqP1ynjN41D/dY/5NmbZaGo7IEg4iWonj6UKv1eOoT8HO2PkcfadS/F4D3XGKL4Guv/w+GDEmzHPZu81Rigc+/hsB7r9ye9dukXHersGuHcAAIDCgQD5KHr00Udp9erVR/MuAWAkQ3gMAACQt3jlMYfHdsLZZE1hM5YMj1XIhnk+/8fhscKby1wj2XZ4HBXUyOGxaVG130N6CSenA5fQEhZRS1Cjund7qK7VJctC8MpeLnXxYaCE3GWZb9cRFNTS3EOGocuGec3Nhvr4LB9VlLqows9lKwZmmoJe/zBI7zZEZM3jxPj2t0TJYWQPjx2aRTXlmV82c3j81v4wOY34+egMVFJT1ENhy6DPVdVnHRsAAEChQIB8FF177bW0dOnSPiuQFy9efDQfBgBGCqw8BgAAyFtctiJ15bFKeGx1thBV1dgKj+VjhXrkymXV8NgMBcnq7rAVHkeiguq7NNKcFo2rLqF2cmcNjy2L6EBbmEIOIl+ZTrOmupPj4zrJdR9lXovdzUFwRCdPaTw8VmqYlzI+Do9LvPwzfbyyub+yH209FsVMQSfUuGh8hZEc34F2/tkyjy9kGcnweIK7h9oPB8SZcHh8zGinbOj3riao2hmmqMCnxwAAoLggQD6KxowZI78AAIaDXPUEAAAAuaEb6uFxNEJmZwsJYdkOj+VDeUrUw+NAB4nudtI03UZ4bFJdl0YxodGk6hJZFqKd+9hlCY8/OthJXWEij4eoskRTGl9PMEJdIZ0cHqEcHnNZjdTxtXHTviw4PD7QblIoKsjj1NPC44FweNwRc9FkT7cMj7kURTYcHu9vjVJM6DTWFaS2WPbxAQAAFBIEyAAABYhXIolwT66HAQAAMGJxwzzV8FiWrRAW6T6/7fCY6f7RyuGxLFuh6bI+s+rK6MbmKFUIjWpKLBkeZ5MMj7tjVOYm8nuE0vjaO4LUHuAgl6i2TCiFxzHTlDWZY6njC6iFx10hS9Y/rlAMt7lsBYfHhiaUw2Ne3SzDY5NkeFzuiA4qQF53oG8jwoFcOf49248BR88jm9pt7X/dZyuG9P6Hmt3xA0Dx0XM9AAAAsEfEovGPpWr4JxwAACCnzW5Vw2MzRpqvQq4kHtRjKVRESG2YZ3B4rDi+WEsjmZZFNSWC/F6F2soiJTz2OWh8pZtUCjZweNzANY91jaq8FrkzNLbLFB43t4VkTWbV8aWGx2UenSpLdOXwWDbM0wRVOMJK4TFr7IzJ8HhchSHDYwAAgGKE9AEAoNBWHkdC8mOpmrc018MBAIBhsmHDBpo9ezb5fD6qrKykRYsW0a5du5Ru++6779Itt9xCZ511FlVVVckw7aqrrhryMY90qeGxLFvhyb6yd7BSw2NZtsJOuG2ZsmGewsJjGc42tXYnw+MJY8uV3s9OhseGTlUVHnIMUFM4tWxFS3tIBtZck1llfCRrHotkeMxlK1TS99SGeRWOCDm0zLWRMzEPh8d+b/YmgAAAAIUKJSx6Wb9+Pe3bt09+z5c8SVqzZk3y+jvuuGN4zxAAQIIZS6485o+lWt2dODYAACPAunXrZOB78skn0/3330+hUIgefvhhmjNnDm3bto2mT58+4O23b99O3/72t2nq1Kk0c+ZMevnll4dt7CMVl4VIC48HUbZi0OGxYtmK5Pj8VVQWdBOFstxGCLLam6k76KPxo+Lhsa4TWQo1jxtC8fCYax43NxtZWvMRhbnJXkMXmZZPNsyr8Dsp3JxtfCQb5oWiH4fHdlYeJxrmHYx4yI7qMh3hMQAAFD0EyBkm6Fu3bk3bdueddya/R4AMALkqW0GxSHzlsdurtLIIAAAKX1tbG914441UW1srw+Ly8nK5/Qtf+AJNmzaNbrjhBnrllVcGvI+FCxdSa2urXLm8d+9eGSTD0OFw1upsIaqqUQ6POfwc7CeTBhMep45PD5cSBbOPT3S1kWX0kM9bThPG+mR4nE1PyKROK0qTKuLhsUrN40hUUH2XRprTkuFxiZd/poEbBwuLqCGg2W6Y1xOx0sJj1bIVqXxurDwGAIDihxIWvWzZskW+u97fFwBALsJjLltBpMUb4qi8YgMAgKKwceNG6uzslCuQE+ExmzRpEi1ZsoQ2b95MdXV1A94Hl63g8BiGHpeFMDtbSAjLRngsyOposv1YZigY/2SSnfB4sOMLtJEVCckazqNHqYXHnWGijqAgXSPl8JhrHtd1abJhHpetiIfH2Rv6HWgLU1dEkw3zKhUb5gUjJnX0WEcUHgMAAIwUWMIGAFAI4TEXGHS5sfIYAGCEeeONN+Qll6vojbc9/fTT9Oabb9LEiROHdByHDh2ipqb0kHP37t1D+piFJllTWFik+/zK4azZfECuJLZbtkJ0t8tPJtkJjwc7Pp6L6C4P6RXVpLdmH19H0KSGgC6D5upKD7ndpnLDvAqhUU2JRRVc9DiQPTyWDf3CRFUuQZUund9vVxpfew+H2xrCYwAAAAUIkAEA8pUMj00ZHnPZChHqzvWIAABgmNXX18tLLmHRW2JbYp+h9Oijj9Lq1auH/HGKoWGe5qsgXaFhXjI87umSK3tt1zw+3BPBVnhsY3zcvI5rHlvOLtJcHtJKK5RW9srwuN0kh05U5TPIYXCJB1OhYV5UPmZNiSC/N/vj8GdDZXjMDf3cRONKBdV18+2E0vg43K7y6eQKZl95HDPxSVQAABjZECADAOQpufLY6ybN7UHZCgCAEaqnJ74y1e1297nO4/Gk7TOUrr32Wlq6dGmfFciLFy+mkS41nJVlITqas0SYvcLjkjIiR/YQuHfDPKO8ikihJ8Jgxscre5tau+mEsh7S/WWkl1Yqr+yV4bFBNK7cogOO7Dfi8HhfSsM8v8Lj8PjbOkJU6YpRmc9BNYabNJvji4fb2W8TsXRq7IzRKdT3Odjv+JA3AwBAkUGADACQrzQt3jAPNY8BAEaskpL4ytRwONznulAolLbPUBozZoz8gizhrJ2yFYfDY6N6PImGPbbCYy5bYbY2Zg+qBzG+RFmI7qBB+ugSMqorSGvYk/WxuCFdIpydNMpJmkJZ52R4bKY0zAtlH19bR5CCEYvKKh00YWw5Wa3dtsJjHl9jB6+KFlnD44/CJWRmr8CRFh63RNXDZgAAgEKATkwAAHlKc5cgPAYAGOEGKlMxUHkLGHoiFjsq4bFKWQgz2J0WHiuVrRjE+JI1hbtj5PMasuaxUkO6GMUb0h0OZ10qK4+jIhke22mYx+MLhi3yugwZHqu8z9473FYZXyI8jgmdqst05fC4MeKloIV1WgAAUFwQIAMA5CusPAYAGPFmzZolj8H27dv7HIvEtpkzZ47445QLsdZGm+Exkehqsx0eM6uj2XZ4bHW22BufRdQQEPGawj4HjR7lUxpfe3eMOkKabEinGs5yTeH6Li0ZHsuGeTbG53XrVOn3KE2VghHTdrgdE1oyPB7rCpLPbSi9OcDhccB0klePZR8YAABAAUGADABQoAQvwwEAgKLGNYbLysroiSeeoM7OzuT2/fv30/PPP08LFiygiRMnJmshv/POO9TQ0JDDEY8glmlr5bEVaCMrErIdHku6oR4eRyNkdraQEJb6ymNBdCBAFIjoMjyWK3sVhtfeEZSlIHjfqlJdMTw2qSWoy5BWNTzmKU/q+Cr9XpWSzLJsRXuPsBduC53aY+5keFzuiCqd3wPtpgyPS40oVTn7lpwBAAAoZPhsDQBAAeKVRSIYIKKxuR4KAAAMocrKSnrggQdo5cqVNHfuXFqxYoWsh/zwww/LAHLt2rXJfXfs2EFnnHEGLV++nJ566qnk9o6ODrk/a29vl5dvvfUWrVmzRn5/yimn0MKFC3EebdL91bbKVnBzXN3lsR8e84u2UTXK4bEsWyEs0n1+5fFxwzxvVKdSl6lcFoLD44bmHnLoRKO8ghxG5p8pUvceBZvj5VbCJtHBzikUC4epOrSH3HvbKZjYsfrMzOOjeNmKQMr46hTeI0nUPOafpcqnK5etaI+5yBSacnhMh8PjrpAlw+MaV5D2h33ZbwcAAFBAECADABTgymP5sVT+LCcAABQ9Do2rqqpkkHzrrbeSy+Wi008/ne69916aMWNG1tu3tbXRnXfembbtj3/8o/xiHDgjQLbP8Pps1TzWXB7SSitsh8dMc2R/2ZbaME/zVZDu8aqtjG5vpu6gj0Y7LRpXqtkKjw1Dp9oqJ7Up3IbD44963GQKIr8RojKVlb38+9sRokpXjEptjC+1YV6Vz5CX2SQb5gmN/I6IWnhMRE0Bi3gRdZlHp3JXkHsgAwAAFB0EyAAAOcCrcUQ0/vHGWEsjOapq5Pea003Rhr3yexHmNTnOviuPw0H5sVSupcgvFAPbf3X4Y61uck08Ie2+E9sAAKCwLVmyRH4NhMtZcCDY25QpUzJuh6HVu2GeXlpJSnUXBvNYKeGxLFvR0SzDV6XxhYh83nIa7yLSFMLZrkCYgqF4eDx5XBkZXSEiUy08jgmiCmeMXJapVLairSNIwYhFZZUOGi3Uxte7YR6X2IhH0WoN8zg89ujZx5d8vLBFtZUOGl9hUDfCYwAAKFKogQwAkAMc8AZ+/wsKvPYrEqFuCr//lvzi7SIWObxTrxXG4uOVx/yxVM0wyOxso/CHu5K3Tdx36v0BAABAbsPjeNmKYQqPbZTVkOPzlMQb5im8MrR6AtTcEUqGx2539vVIpqUlw+MaT4S8CkuYODzmshXBsEVelyHLVmhD1DAvNTzmshV2wmNW4tZleDyYleUAAACFAgEyAECOaC43OUfXxr93OOXXgA3zImG58pg/Assv9pL3k+G28v+N/u8PAAAAhgYv9hZdbb3C46EJF/mTSfbD4/Tx6RXVSg3zrFAPmR0tZOjq4XEsJqg14kiGx+Wu7Cvh+f3zhoCgru4Yed06Vfo9SmUrOsNkv2GeKdLCY9WyFalGl+oIjwEAoOghQAYAyBHZEEelpqFlHi5nIeIrjwcImpO3EURWR9NRGikAAACokDWFA21kccM8G+GxGeweVHgsP5lkc+XxoMYXCpLV3UGkG1RTXaIUHkdMopZuk/jzVMrhMREdCBAFIjqV+RxU6fcqVf1o745RQ0CPN8wr1ZXD45aAdUThsYSVxwAAMAIgQAYAyBG18NgiEQ7Fl+M4XGkrjwfC4TGvFAIAAIDhkSgLITicdXnUw9lAB1kdzfYeKxohs7NFfjLJbtmKwYxPdLeTpumyZ4PLmb0jXSQmqK5Tl6Uoyh0x5fCYG+YFojqVuqx42QpSa+jHdY4deqJhnkLZipiIh9tCHFl4DAAAMEIgQAYAyFPx8Dgow2MuW0GGet9TDo9Vw2YAAAA4Mqk1hflvtlZWqRzOmi0NcmWv7ZrHh3si2K15bGd8XPNYjk/TSS+vUnrzm8PZ/a1RillEfq9GXoew0TDPpFKnReNKNaWyFRweNzT3yPB4YrmlHB7z+Pgx/SW6WniMJpQAADDCIUAGAMhTIhJOhscqZStScXis+0cP2dgAAAAgc8M8vdRmeGw4ZFkruw3zNF+F0pvFgx1fVyAsax7z+Ay74bFJNK7UohJP9mCcg9zGoCPZMG98KSmFxz3BiAyPuaHfpConuRQy+NTxVZRoVOLSlbLjpkCvxsYAAAAjDAJkAIB8ZSM87r0whsNjlOQDAAAYWr3D2XhZCLIVHjvHTlIra5USHsuyFR7vkI2PV/Y2d4TkymjV8XFN4WR4XGFQuZuUw+NAzJFsmKcphsftgagMj7mhn8up2wu3KwwZVqvMrxojXuoJ2wuQu031T40BAAAUAvxlAwDIU5rTrRgeiz4N8xAeAwAADC0OF/uGs/ZWHstw1unK/lixWHp4bLNshZ3xJcpCGLpX1jzWFD4ExeUq2gMW1fjj4azfa1C0Pcv4LJEMj0sdMfWGeR1Bag8YpGuaDI+5oV+sy164zeNTDY8DppNK3OrrrjpjTmqNKqTnR2jdgROS33s2ZTnYh4VSbpPNlePfG/R4eksdX8PeThqJHlE8R8Uy/us+WzFkYwGA3MAKZACAPMEvDlOprPbhVzeiqw0N8wAAAIaZbFhrM5zlHgV2w2MWa220GR5TfH5gc3wdQZEsC1FTXaJWtiJqUWtQkw3p1MNZQY1maTI8rvHGlMLj5Ph0jaorPTI8zobD45aAZSs8ZonwuNSI0uhSXTk8PhjxkqFlr/sMAABQSBAgAwDkSXgsXxzauQ2/OoxFyeJu6miYBwAAMKxkw1o7K49DQbK6O2yHx/EHM22tPLYCbfH5ga3wmKixR08pC5E9aA2HY7S/JUqWiDekUwlned9O4aGA5UqGxyo1j1PHV1XhIYdhKJWtaOk2bYXb8rFMVzI8rnEFlT7a1RE0ZXjs0Cwa7QwpPQ4AAEChQIAMAJAHZHhsmfbC40iIhBUj3eVBwzwAAIBhxm/e2ilbIbrbSdN0++Gx7G1QbatsheDw2OVRHh83zGvs0cihiWRZCJXweF9Dlyxf4fcIpYZ0XPO4I2JQyHJQqR5RDo+5bEXq+FTDYy5bwY+pGm7z9Koj5qKQaSTDY5WyYBweN7SbMjye4O4hp46mewAAUFxQAxkAIMcrjzV+DWmZ8sWh0m0Ol62Qt9UdpJVxN/UhHyoAAAD0aVhro+axppNeXmU7PGaG12er5rFswltaoVZWoydAzR0ajdUE1ZYJW+GxaXLdY4O6HOoN80KWTh49RjVGgHTdp9QwryHUI8Nj1fGlNsyrKNHIqxBuJ2oehyyDPIapHB53h035eA6DaIy7h1wIjwEAoAhhBTIAQB6UrZArixRfHPLKY/5YqqxL6HAqvTgEAACAo0vlz29qwzyDw2OV/gaD0Lthnl5aqR5ud7SQoes0kcNZZ/bbRKJmMjweV11CFT6HcnjMNY89ukXlWog0XVMKj9sDUVm2QnV8vRvmeV32GuZ5dJP8RkT5zfnmLkuGx5NGOREeAwBA0UKADACQB2UrVMLjRMM8Dp75Y6nEXwiPAQAA8lJqeCzLVgxTeBwvW2FjfLohG+a5FMJZnoM0Nvd8HB77vQrj+zg85prHfpepFG5z2Yr2QIQ4Z47XZNaGpGFeanjMZSv8jgjZYSTCYwfe0AcAgOKFABkAIFdslq1INMzjF6DxshV4oQIAAJCvDfbSwuNBlK0YfHis2Rqfo6pGqWGe/ORUSyOZlo3wmGseR41keCxrHivVFBbU0NxDhq5RdaVHrWyFSbYb5vHxSw2PZcM8m2rKHQiPAQCg6CFABgDI1T/ANstWJBrm8cpjpY+lBruP0kgBAABAlRkKktXdYSs85nDWLp4e8CeT7IbHfcansDKax2d1tsg3v6v9HuWVx+3dMQqZ+sfhscKrz44gUWOPLstWVFV41BrmRS2q69RtNczjAbb1iLTweDDvzTsMvKEPAADFDwEyAECOqIbH3Ek9vWGeWnhsdTQfpZECAACACi4LIbrbSdN09fA4Gkn2RFDF8wMr0CY/mWQrPB7k+MzOFhLCIsNfRWWlbqXxNQQ0CsWIPIalHB5zzePGHk02zOOyFSrhMTf0298SpZgVb5hXotQwj8Nji0JR64jCYwAAgJECATIAQJ5Krjw2Y7Yb5snwWFdYfQMAAABHRbKmsKaTzg3zFMPZ6MH9yZ4IdspW8BvM/MkkO+Gx7fHFYvHxCYt0n5/0klKl8R1oN6kropHHQeR3msrhMTfM4/C4lhvmKZSt4PCYG/pxeDyu1FJsmBcfXygqyOPUER4DAAAoQIAMAJCn+EWlXHlsOOw3zNMNcoyqGcrhAQAAQIaGeQaHsyplIRLhsRmz1RMhUfNY45JWqp9MGsT4IlFT1jzm8Wm+CtI9JerhcciiMpegCp9DafrSGdGSDfMmcnis1DDPlOExN/Sr8RtUnn1hdNr4PE6NKks0pfF1h9UDfgAAgGKEABkAIE8lVh7LF4g2P1fJ4fFQdXsHAACAzOGsck3hlPDYqBqnXNYqtWGeXmo/PFYdH6/sbWzukSuj5fg8XnvhsUencaWCFzsrhceNIVeyYZ5LMTxubgvJ8Fg29PM5bI+vskRXOn6dMSc1d1lkR9TCy2wAACgu+MsGAJCnEiuPB9MwD+ExAADA0OsTztopW5EIj0v92W8jKC08jpetGJrxJcpCmFa85rHS+IhkQ7pEODu+wlBeeczhsUMj5YZ5PL6W9hBZguLhsVJDv/TwmMdHiuHxwYiXFIaVFLF0aop61G8AAABQALA8DQAgT/CLw1T8Ik8lPLZCPUSx6NANDAAAADK+ecthsK3wOFFT2EZ4zKyOJiJZg1i9YR7PD2yHx1FBjYfLQlT7PaSXcHIaG/hnsog6QhqFKTU8zj6+rpiTmg6HxxNKwtShEh4nxmf5qKLURRV+p9IEq2+4rR4eOzSLasodyuHxR+ESMgU68gEAQHHBCmQAgDwJj+WLwxRKZSvMGFndHWiYBwAAMMxkw1qb4bHV2WI7PJaPFeqxFR6boWB8fmBjfJGooPouLVkWoqw0e1FhyyI60BamUIxkQzrVcDYkDDpolibDY7fCCt/U8XF4XOJVOOYyPLYoFLUXHocsIxkeT3D3kMPIfptITMjwOCZ0GuUMZ/+BAAAACggC5Aw2bNhAs2fPJp/PR5WVlbRo0SLatWvX8J8dABgxODyWK4ltELzqOBYhTdPRMA8AoMgd6fy0p6eHbrvtNpoyZQq53W55uWrVKrkdBkk3bJWtMDtbSAjLdngsH8pToh4eBzpIdLfL+YF6eGxSXZdGMaEpl4Xg8Pijg53UFSbyOOhwQ7rs4wvGiDpMDxkklMNjLluROj7V8JjLVoSiwl64bRnUEXMlw2OXbimFx/tbozI8HusKks8YeNU2AABAoUGA3Mu6devowgsvpO7ubrr//vvp9ttvp507d9KcOXPor3/9a27OEgAUfdkKubJIobt5Au8vIiFep0y6Yjd1AAAYmfNT0zTp3HPPlbedN28ePfLII7Rw4UJ64IEH5KXFSSDYJhvW2ql5LCzSfX7b4THT/aPtNczT9Pj8QHFlNDfM43C2psSyFx53x6jMTeT3CLWyEBGN2qMOGR7XOjqUwmNumMc1me2ML7XmscepKYfbXLaCw2NDE8rhccw8HB6bJMPjcgfKigEAQPFB4pCira2NbrzxRqqtraVt27ZReXm53P6FL3yBpk2bRjfccAO98soruTpXAFCkZSv0MRNleMwvDpVuF4vGP5bKrc1dboTHAABF7GjMT59++mnaunUrXX/99fTQQw8lt/Mq5JtvvpmeffZZuuyyy4b8Zyk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      "text/plain": [
       "<Figure size 1440x1536 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: decision_margin_distributions.png\n"
     ]
    }
   ],
   "source": [
    "fig, axes = plt.subplots(len(subjects), len(PAIRS),\n",
    "                         figsize=(6 * len(PAIRS), 3.2 * len(subjects)),\n",
    "                         sharex=True, squeeze=False)\n",
    "fig.suptitle('LDA decision-function distributions on ONLINE sessions\\n'\n",
    "             '(class separation ↔ classification amplitude & SNR)',\n",
    "             fontsize=12, fontweight='bold', y=1.01)\n",
    "\n",
    "for i, subj in enumerate(subjects):\n",
    "    for j, pair in enumerate(PAIRS):\n",
    "        ax = axes[i][j]\n",
    "        for cond, color in cond_color.items():\n",
    "            row = next((r for r in results\n",
    "                        if r['subject']==subj and r['pair']==pair['name']\n",
    "                        and r['condition']==cond), None)\n",
    "            if row is None: continue\n",
    "            m_mi   = row['margin'][row['y_test'] == 1]\n",
    "            m_rest = row['margin'][row['y_test'] == 0]\n",
    "            ax.hist(m_mi,   bins=15, alpha=0.5,  color=color,\n",
    "                    label=f'{cond} MI',   density=True)\n",
    "            ax.hist(m_rest, bins=15, alpha=0.25, color=color, hatch='///',\n",
    "                    edgecolor=color, label=f'{cond} REST', density=True)\n",
    "        ax.axvline(0, color='k', lw=0.8)\n",
    "        ax.set_title(f'{subj}  |  {pair[\"name\"]}', fontsize=10, fontweight='bold')\n",
    "        if j == 0:                     ax.set_ylabel('Density')\n",
    "        if i == len(subjects) - 1:     ax.set_xlabel('LDA decision function')\n",
    "        ax.spines[['top','right']].set_visible(False)\n",
    "        if i == 0 and j == 0:\n",
    "            ax.legend(fontsize=6.5, loc='upper left', ncol=2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('decision_margin_distributions.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()\n",
    "print('Saved: decision_margin_distributions.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcb6d19d",
   "metadata": {},
   "source": [
    "---\n",
    "## Figure 3 — Paired Δ (FES − NOFES) per metric\n",
    "\n",
    "Within each (subject × pair), FES and NOFES sessions use the same offline-trained model. Positive bars mean FES > NOFES; negative means NOFES > FES. This removes the offline-model-quality confound and isolates the effect of feedback type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "75df404b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T19:27:27.545755Z",
     "iopub.status.busy": "2026-04-22T19:27:27.545655Z",
     "iopub.status.idle": "2026-04-22T19:27:27.826448Z",
     "shell.execute_reply": "2026-04-22T19:27:27.826020Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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V8+hYrVIKavxb2rVrZ48//rgrLaBG09WQuUelFlT6QO9pWfWqceXf//63W3bRcvnLVaiEgzcfKu0QzYoVK+zuu+92f6sMwT333GMvvviiK8PglaJWY+ORbN682aZMmeLmoWnTpsFG6l977bWon1fS7/DLL7+0q666KtiQe79+/VwJDy2vyid4JULef/991wi9R43C63vVZ6gx9SpVDny3qkbcf/e737nvYN68eW47VjkWNeCuEhkq9bB06VK37Xglz6ZOnerKOsc7j9rW9N2JfnP6Ljze3/rMvn37HvDyADh4OI5yHC2v42h4aUkdn3Q8ueCCC4LnJDovOu+889x26pUF0zHoySefjDrf999/v73wwgvufEry8vJs3Lhxcf8m4jmPmjFjRomnp/OF66+/3i2XaDl13Fa/Tz75pMjwKkE2ZMgQV5ZJ5cQmT57sjudjx44t9jwIQNkc78LpukTnyP5O+71Y4r0O9NN+s2PHjpabm2vXXXddsP/DDz8c/Fv7Pk1LunXrFrwuUQk40XWVrq/CqVRc1apVXXk77eOHDh0a17JHG0/NHmm+dC2hdaFjwZgxY9x7Knv4yCOPuL+1H1W5a49KHHr7WO1/o9F+3tuX6hpF18Xal+qaRfQdX3rppcF9bSR6T9c13vagsseahkoeapp6f9SoUe6zwqkpJ5XY0/J5x6vw76KsqTyz1r1H5Zs9Wse67hMdV1U6Ucsnuq/wzDPPlPjzVGJWJSF1TPRKSepaWPcnItE8qDyijlnefQAdr3Tckl69ernjvYbxmhjSOUek7dNP28WaNWvc3yp56F37qnSnaLvwvrN4rm81DW1v3vjibYPqYint8mzdutVOOOGEmL9lVDxlcfxJpN27d7t9g87TVapWdG6q/btHpd51b/CVV15x+3Zt817pUe1z3n333YjT1vFOZUdzcnKC98h0X8orU6rjh1datWbNmjZ9+nR3Hq/fpXdfsqRU2l73wrTezzzzzGBJXO3XS/MbPvnkk4PjLV++PORVNJz2r9ovevsg77pFxxOdz+taR3Q81L5R09d+XOtex6NIxyzd59P8a33ovp6a4ItHtPHiuUZr3Lix2//pmOzRsbq4e8mi707leUXXj7pe0+c1a9bM9dN79913nx0oXRu98847bj3qnqxH61FlgSN57LHHipyv6VzBf1ytU6eO+1vfo0oAq3nDVq1a2dVXX23//Oc/raxo+z/00EOtR48eJRrPO+/S8UQlfr1SzRdddJH7LZUVrX+dD5x11lk2aNAg97/2aytXrnS/d33vKqPt0Xs6L9W+IxaVePbOj6Vnz55xz5POx3RcV5Ob3rFfv0EvLuHn7W9+8/+bsyzN5+r82n++cPrppwff03pJ9Odp/6LvW/sx3RM46qijLCFKFRoGyohK7XhPLqissag0RLQnliO54447gsP/8pe/jOtzoz0pp6dqzzvvPPdEmMpihD/NkZub64Z76KGHgv2mT5/usi4juemmm4LDqVyh/2lZv2hPmMYqdRQpy0RZvP7Ml1hPLqmk8xVXXOGyOyM9EeZv9D5W2cJo61Plyrx+Y8aMCQ6rJ3O8z9PTj16pF/8TSXrSzuPfHrSuixPvd3jfffcF+6mMZTT+p/5UkiGaA8nkVfnuSF566aXA6aef7sreRMo41tNWJZlHfefek+t6akxUDsV7ClXlwACkFo6j/4fj6ME/joq/v1dic9WqVcF+Ku+k0lOi8yCvv5549vjnWyWMPJqe119l5bwnu+PN5I3VP9JnF5fJq6f0vX4qF+nPitZxPPxpe5U89fqp1KQyB7xOWdXeeVB4CSoAZXe8i+fpff++INI+It7rQP/+R9kpX3/9teuvsq7aN3rZGh6VRvSGV9UAb3/hr7ijZnU8/nmOVMYymuLG07WZSh0qkypSxuL5558fHDZWKdxo608lOL1++k492sf7s7LCm87x+/DDD4PDaRx/5o9/O9H1Xvh8qhkcL7tN34nXX9myByuTVzQf3vvKOPLMmDEj2N/LmlH2uNdPmTbx8B8r/VmmXllJL1ss0u/iz3/+c5Ftwrte1HW8/56GrvPDzz0iHXu13au8ufd7UEltbxtXKWlv+HvuuadE17fh8x4u0jZ6IMsTz28ZFU+ijj8lyeRV5rv/3FGdzrEjbff+SgT6vXv91USAZ+3ate5+j1fePnzf7i9b75+2zmc9Z511VrC/1wzMVVddFez3hz/8IeJ5fKTfZjj//TF/prDWg/c7U6d7WiX9Deu+aPgxzKuQoNLxel2zZo079nrD6VgVfo9V4/i/D3/p/ffff7/INYTWYzxVlOIdryTXaLGaS4i2LfozwnUv0qO/vf46VykN/744vNOy6N65/1gevk1E6sLPO3StGalqivcZCxcuTHgmr6pNabzf/va3xQ4bnslbWFjoqkN427z3ns5x/N9fojN5vWoiTz31lPtfmaj6X803KmtV+zbv9+VtOzpO63cYz3S9zqsOGg+tv0gVBv79738XGVbX4VpvP///EtMH8rn+DGovczi89HIiPs8791Xzi17zDmTyokLRk3DeExHKIPSeNDr//PODw+hptFhP8Uq9evWCf3/11Velnh89taKnufUUjZ4IKywsLDKM9/SWslK8rFg9TaanperXr++WwZ81ouzM6tWru7/1xKmeqlPD7f3797fFixdbon366afBv/VEl56Cibbuu3fv7p4U+vzzz92TO9GWNVHz4tE68J4g1nerp/TCnXLKKcG//dnHxc1TSb5D//x5T+QUtxznnHOOlYVI09UTS3rKXNkCeqrLyzgubllizaOextPT+aKn//RUt56I9n5jF154YUKWB8DBwXGU42h5Hkf99PSyV2lC50MeZaB5T1V7mQWx5sN/vqDpZWZmur/z8/MP6BwvEfRUvT8rWNm5/qfHw/mPza+++qp7ytfrlFXtfX/r1q0r83kHUl2ijnfh/Nkt8WS5lOQ60K9169buGlCUqeDt2/z7Qv8+Q5kV3v5C1Zg8qrYUTllL/gyEeEUaT9cbp512msuGUnaaP1OhrK8Rlb3jX/f+4WJNQ1mV/swf//440jSUjeJdn5fk+JhIqsak67tI9zOUNeXxMnh1LG3fvn3wOO0/HsXDv57960fHoki/GV2D+ilrxRuuZcuWIeutuPXt0fLu3LkzuPzazrxtXBmC4dt4WV6DH8jyxPNbRsVSVsef4ig72H/uqM7bJ8T7G/f2FapgoPt/ysBVtqeyycJF24aLu6bw74+87Obw8/iS8i+P9o9eRQvv80r6G9Z9Uf12vQxeZd+qqoAyVHVvVpTJ7M9m9rLz/PsBVV3wfx/+bOBIx2dlBPqvF+IVabzSXqMl4vgc736+tLQsyqaOtEyRqoVEqxqi34e2DVVGUSa0dw3qfca1116b8Hn39g2lOU4pE9yrtvjEE0+41xNPPLHY89AD5V1Tt2nTxr16FTYmTZrkznFvvPFG+/Wvfx0yjrZHr0ppNP5zGf/nxEOf/eabb7rMdX+FAZ0r6D6Af7+o6WqYtP9fOedAPlf3xb3tQsd2VQ+oW7duQj9P5z6qNKNzVcVgElEN1EO5ZiQNlZ7wfqzaAeuESY499thgOQqdjPgPnJGo9JVHZRm8G2cl9dBDDwVPdrSDVikCHTi8Esbi7dRUeuSDDz5wOz+VENBJhX642kHoolwnT165ZA2nUj46SGpnsG3bNld+S8scrSRKWdPne+UWdbKjk1Itq7/0RnE78AMRq0y1+E8G/Sc3xZ04l+Q7LMtl8gdk/Rfw0XgXin4PPvhg8G+VIVEpk/AyEqVZFpW6Eq0nHUC9Us1a5+FlKQAkN46jHEeT5Tjqv9jxX7j4L5L84r0RVtz5Qnkeu6NNp6R+/PHHUo8LVBaJOt6FU+BM13L+LvzmTbh4rwP9wm90l+amb7T9hcoRlkak8ZYtWxYsO6rgtcojqjyjP/BYnteI5Xl8TCRtp/71qAeHZNOmTSHbsG72emUpvZuwEmkbS+Q6jnRteiDTS6VjYkm2nwP5LSN1lNXxxy+R+59I27D25Wr+RE466SS3TDq/95oQibVvP5B9ZqL2DyWZTrRhvZLNCpDqPpjO+7Uu1Im+P+87POSQQ0ocaIu07yrJvrS48ZLlXmciNG/e3M2rAuNeAF8BPTXfEOucJfx8zXvAOPwcTfffvaQWlbH25l/BOK/EdyKDvLr29ZfHLun9Wf/6jbfJjUTwfs9eM4Da7iNdw+u3ruZE9L3Fou/DX2Za55Tx0nag36iS45SI5DXDqRiPV0I8UunkA/lclbTXebseAND4ujcevoyJ+DyVttf+Qb9fXXd453UKanu0/eih+ZIiyIuk4b9YHDBgQMh7/qfiiruI0UHZayNAB4qbbrop4nCRnqzy087Dc9ddd7kfmQ4capMmnHZy+vH/6U9/cgdW3QxctWpV8H2vbryG04mfniR677333JNVqv3uzatOrhJJJ5r+J72i3aT0L6vaJ9COTcvqf0LGz3/DNt4Th/B58ehA+9lnn7m/tWNTGwmJUpLv0D9/sdoW8A/n7eAj8d8Q8p846OSiNCdN/mVRm1cK7urpS3//ks6jqM1dL5tK7Sx4bX/pN+i1sQIgNXAc5ThansfRsuA/X1CGsteWlzLOjjjiiBJPr7jzl5Icu70qJN6T/P6gcKR2+/zHZrXlpXPC8E4XfF5bYwDK/niXCPFeB5aUf5+hTKVI+wzvvD0RN1+Lu/5Qm3C6eRyrrbFEXiPq5pe/XUv/cLGmoXH82T/+/XGsaZQXVdDSAwIeXd81adIkuP3GEzgp6XbuX8/+9aM24SJtB+H9dJ7h9dN1vK7nS7q+df3pBYt0I1nZkeHbt46raju4pNe3Jd0OE7E8qDzK4vizffv2YGBFvAQM8fYBamc2/DcSLaEl2m/c3wa3R+1sK1tP5/deu7oHwn9+rLa0I53Hl5R/eTSPqizh/7zS/Ib9xzIvuUX3klXdQdNSG7feMUj7Za/ddP80lNUc7Xx+2LBhB+34HM8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      "text/plain": [
       "<Figure size 1920x540 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: fes_minus_nofes_delta.png\n"
     ]
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 4, figsize=(16, 4.5))\n",
    "fig.suptitle('Within-pair Δ = FES − NOFES  (positive → FES better)',\n",
    "             fontsize=12, fontweight='bold', y=1.03)\n",
    "\n",
    "for ax, (key, title, _) in zip(axes, METRICS):\n",
    "    labels, deltas = [], []\n",
    "    for subj in subjects:\n",
    "        for pair in PAIRS:\n",
    "            fes = next((r for r in results if r['subject']==subj and r['pair']==pair['name']\n",
    "                        and r['condition']=='FES'),   None)\n",
    "            nof = next((r for r in results if r['subject']==subj and r['pair']==pair['name']\n",
    "                        and r['condition']=='NOFES'), None)\n",
    "            if fes is None or nof is None: continue\n",
    "            deltas.append(fes[key] - nof[key])\n",
    "            labels.append(f'{subj}\\n{pair[\"name\"].split()[0]}')\n",
    "\n",
    "    colors = ['#E05C2A' if d > 0 else '#2A7BE0' for d in deltas]\n",
    "    ax.bar(np.arange(len(deltas)), deltas, color=colors, edgecolor='white', zorder=2)\n",
    "    ax.axhline(0, color='k', lw=0.8)\n",
    "    ax.set_xticks(np.arange(len(deltas)))\n",
    "    ax.set_xticklabels(labels, fontsize=8)\n",
    "    ax.set_title(f'Δ {title.split(\"(\")[0].strip()}', fontsize=10, fontweight='bold')\n",
    "    ax.grid(axis='y', alpha=0.3)\n",
    "    ax.spines[['top','right']].set_visible(False)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('fes_minus_nofes_delta.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()\n",
    "print('Saved: fes_minus_nofes_delta.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f9533f8",
   "metadata": {},
   "source": [
    "---\n",
    "## Figure 5 — Per-class accuracy and EARLYSTOP latency\n",
    "\n",
    "FES stimulation fires only on MI trials, so if it helps the subject/system it should\n",
    "show up in **MI-accuracy** (count_240 / count_200) specifically, not in REST-accuracy.\n",
    "A lift in MI-acc without change in REST-acc would be a genuine sensitivity gain; an\n",
    "equal-and-opposite shift would be a bias/threshold effect.\n",
    "\n",
    "**EARLYSTOP latency** (BEGIN → EARLYSTOP in seconds) is a continuous readout of how\n",
    "confidently and how fast the live classifier committed — shorter latency means a\n",
    "sharper MI signal crossing the detection threshold earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "086ef172",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T19:27:27.828111Z",
     "iopub.status.busy": "2026-04-22T19:27:27.828016Z",
     "iopub.status.idle": "2026-04-22T19:27:28.197602Z",
     "shell.execute_reply": "2026-04-22T19:27:28.197189Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/stats/_axis_nan_policy.py:430: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n",
      "  return hypotest_fun_in(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1680x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: per_class_acc_latency.png\n",
      "\n",
      "Per-subject paired Δ = mean(FES) − mean(NOFES) & T-Test:\n",
      "\n",
      "  MI accuracy:\n",
      "    subj 002:  FES=0.93  NOFES=0.85  Δ=+0.08   |   p = 0.5000 (t = 1.00, n = 2)\n",
      "    subj 003:  FES=0.73  NOFES=0.72  Δ=+0.02   |   p = 0.8656 (t = 0.21, n = 2)\n",
      "    subj 005:  FES=0.78  NOFES=0.82  Δ=-0.03   |   p = 0.9208 (t = -0.12, n = 2)\n",
      "    subj 009:  FES=0.82  NOFES=0.72  Δ=+0.10   |   p = 0.3743 (t = 1.50, n = 2)\n",
      "\n",
      "  REST accuracy:\n",
      "    subj 002:  FES=0.87  NOFES=0.83  Δ=+0.03   |   p = 0.7048 (t = 0.50, n = 2)\n",
      "    subj 003:  FES=0.78  NOFES=0.76  Δ=+0.02   |   p = 0.5000 (t = 1.00, n = 2)\n",
      "    subj 005:  FES=0.93  NOFES=0.90  Δ=+0.03   |   p = 0.0000 (t = inf, n = 2)\n",
      "    subj 009:  FES=0.72  NOFES=0.85  Δ=-0.13   |   p = 0.1560 (t = -4.00, n = 2)\n",
      "\n",
      "  MI EARLYSTOP latency:\n",
      "    subj 002:  FES=2.50  NOFES=2.30  Δ=+0.20   |   p = 0.6020 (t = 0.72, n = 2)\n",
      "    subj 003:  FES=2.45  NOFES=2.36  Δ=+0.09   |   p = 0.8457 (t = 0.25, n = 2)\n",
      "    subj 005:  FES=2.54  NOFES=1.99  Δ=+0.56   |   p = 0.3678 (t = 1.53, n = 2)\n",
      "    subj 009:  FES=2.60  NOFES=2.29  Δ=+0.31   |   p = 0.2213 (t = 2.76, n = 2)\n",
      "\n",
      "  REST EARLYSTOP latency:\n",
      "    subj 002:  FES=2.11  NOFES=2.22  Δ=-0.10   |   p = 0.7758 (t = -0.37, n = 2)\n",
      "    subj 003:  FES=2.34  NOFES=2.64  Δ=-0.30   |   p = 0.5264 (t = -0.92, n = 2)\n",
      "    subj 005:  FES=2.20  NOFES=2.48  Δ=-0.28   |   p = 0.3581 (t = -1.59, n = 2)\n",
      "    subj 009:  FES=1.89  NOFES=2.23  Δ=-0.34   |   p = 0.0667 (t = -9.50, n = 2)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/stats/_axis_nan_policy.py:430: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n",
      "  return hypotest_fun_in(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ttest_rel\n",
    "\n",
    "PER_CLASS_METRICS = [\n",
    "    ('mi_acc',       'MI accuracy',               '0–1'),\n",
    "    ('rest_acc',     'REST accuracy',             '0–1'),\n",
    "    ('mi_latency',   'MI EARLYSTOP latency',      'seconds'),\n",
    "    ('rest_latency', 'REST EARLYSTOP latency',    'seconds'),\n",
    "]\n",
    "\n",
    "# Restructure data to average across multiple sessions (Pairs) for each subject\n",
    "def subj_cond_metrics(key):\n",
    "    \"\"\"Return {subj: {'FES': [vals...], 'NOFES': [vals...]}} using non-null entries in `results`.\"\"\"\n",
    "    out = {s: {'FES': [], 'NOFES': []} for s in subjects}\n",
    "    for r in results:\n",
    "        v = r.get(key)\n",
    "        if v is None or (isinstance(v, float) and np.isnan(v)):\n",
    "            continue\n",
    "        out[r['subject']][r['condition']].append(v)\n",
    "    return out\n",
    "\n",
    "mi_acc_data = subj_cond_metrics('mi_acc')\n",
    "rest_acc_data = subj_cond_metrics('rest_acc')\n",
    "mi_lat_data = subj_cond_metrics('mi_latency')\n",
    "rest_lat_data = subj_cond_metrics('rest_latency')\n",
    "\n",
    "metrics_data = [mi_acc_data, rest_acc_data, mi_lat_data, rest_lat_data]\n",
    "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
    "fig.suptitle('Per-subject average: MI vs REST, accuracy & decision latency\\n(mean across that subject\\'s sessions ± SEM)',\n",
    "             fontsize=13, fontweight='bold', y=1.03)\n",
    "\n",
    "width = 0.38\n",
    "x = np.arange(len(subjects))\n",
    "\n",
    "for ax, data_dict, (key, title, unit) in zip(axes.ravel(), metrics_data, PER_CLASS_METRICS):\n",
    "    for i, cond in enumerate(('FES', 'NOFES')):\n",
    "        means = [np.mean(data_dict[s][cond]) if data_dict[s][cond] else np.nan for s in subjects]\n",
    "        sems  = [np.std(data_dict[s][cond], ddof=1) / np.sqrt(len(data_dict[s][cond]))\n",
    "                 if len(data_dict[s][cond]) > 1 else 0.0 for s in subjects]\n",
    "        offset = (i - 0.5) * width\n",
    "        ax.bar(x + offset, means, width, yerr=sems,\n",
    "               color=cond_color[cond], label=cond, edgecolor='white', capsize=4)\n",
    "        \n",
    "        # Overlay individual session values\n",
    "        for xi, s in zip(x, subjects):\n",
    "            if data_dict[s][cond]:\n",
    "                ax.scatter(np.full(len(data_dict[s][cond]), xi + offset), data_dict[s][cond],\n",
    "                           color='k', alpha=0.5, s=14, zorder=3)\n",
    "    \n",
    "    # Calculate p-values for pairs and add them to custom labels\n",
    "    new_labels = []\n",
    "    for s in subjects:\n",
    "        label = s\n",
    "        if data_dict[s]['FES'] and data_dict[s]['NOFES'] and len(data_dict[s]['FES']) == len(data_dict[s]['NOFES']) and len(data_dict[s]['FES']) > 1:\n",
    "            try:\n",
    "                _, p_val = ttest_rel(data_dict[s]['FES'], data_dict[s]['NOFES'])\n",
    "                p_str = f'p={p_val:.3f}' if p_val >= 0.001 else 'p<0.001'\n",
    "                label = f'{s}\\n({p_str})'\n",
    "            except Exception:\n",
    "                pass\n",
    "        new_labels.append(label)\n",
    "\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(new_labels, fontsize=8)\n",
    "    ax.set_title(f'{title}  ({unit})', fontsize=11, fontweight='bold')\n",
    "    ax.grid(axis='y', alpha=0.3)\n",
    "    ax.spines[['top','right']].set_visible(False)\n",
    "    if 'acc' in key:\n",
    "        ax.axhline(0.5, color='gray', linestyle='--', lw=0.8, alpha=0.6)\n",
    "        ax.set_ylim(0, 1.05)\n",
    "\n",
    "fig.legend(handles=[Patch(color=cond_color['FES'],   label='ONLINE_FES'),\n",
    "                    Patch(color=cond_color['NOFES'], label='ONLINE_NOFES')],\n",
    "           loc='upper right', ncol=2, bbox_to_anchor=(0.98, 1.0))\n",
    "plt.tight_layout()\n",
    "plt.savefig('per_class_acc_latency.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()\n",
    "print('Saved: per_class_acc_latency.png')\n",
    "\n",
    "# ── Paired Δ (FES − NOFES) summary per subject ─────────────────────\n",
    "print('\\nPer-subject paired Δ = mean(FES) − mean(NOFES) & T-Test:')\n",
    "for (key, title, _), data_dict in zip(PER_CLASS_METRICS, metrics_data):\n",
    "    print(f'\\n  {title}:')\n",
    "    for s in subjects:\n",
    "        if data_dict[s]['FES'] and data_dict[s]['NOFES']:\n",
    "            delta = np.mean(data_dict[s]['FES']) - np.mean(data_dict[s]['NOFES'])\n",
    "            if len(data_dict[s]['FES']) == len(data_dict[s]['NOFES']) and len(data_dict[s]['FES']) > 1:\n",
    "                t_stat, p_val = ttest_rel(data_dict[s]['FES'], data_dict[s]['NOFES'])\n",
    "                stats_str = f'p = {p_val:.4f} (t = {t_stat:.2f}, n = {len(data_dict[s][\"FES\"])})'\n",
    "            else:\n",
    "                stats_str = f'n = {len(data_dict[s][\"FES\"])} (too few paired runs)'\n",
    "                \n",
    "            print(f'    subj {s}:  FES={np.mean(data_dict[s][\"FES\"]):.2f}  '\n",
    "                  f'NOFES={np.mean(data_dict[s][\"NOFES\"]):.2f}  Δ={delta:+.2f}   |   {stats_str}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c74da761",
   "metadata": {},
   "source": [
    "### Per-subject average latency\n",
    "\n",
    "Collapses the per-(subject × pair) bars into one FES value and one NOFES value per subject\n",
    "by averaging across all that subject's ONLINE sessions of each type. Error bars are the\n",
    "session-to-session SEM within that (subject, condition). Easier to read when you just want\n",
    "the per-subject FES vs NOFES comparison without the pair split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4ea9951f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T19:27:28.199503Z",
     "iopub.status.busy": "2026-04-22T19:27:28.199411Z",
     "iopub.status.idle": "2026-04-22T19:27:28.407015Z",
     "shell.execute_reply": "2026-04-22T19:27:28.406594Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x540 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: per_subject_latency.png\n",
      "\n",
      "Per-subject paired Δ = mean(FES lat) − mean(NOFES lat) & T-Test:\n",
      "  MI latency:\n",
      "    subj 002:  FES=2.50s  NOFES=2.30s  Δ=+0.20s   |   p = 0.6020 (t = 0.72, n = 2)\n",
      "    subj 003:  FES=2.45s  NOFES=2.36s  Δ=+0.09s   |   p = 0.8457 (t = 0.25, n = 2)\n",
      "    subj 005:  FES=2.54s  NOFES=1.99s  Δ=+0.56s   |   p = 0.3678 (t = 1.53, n = 2)\n",
      "    subj 009:  FES=2.60s  NOFES=2.29s  Δ=+0.31s   |   p = 0.2213 (t = 2.76, n = 2)\n",
      "  REST latency:\n",
      "    subj 002:  FES=2.11s  NOFES=2.22s  Δ=-0.10s   |   p = 0.7758 (t = -0.37, n = 2)\n",
      "    subj 003:  FES=2.34s  NOFES=2.64s  Δ=-0.30s   |   p = 0.5264 (t = -0.92, n = 2)\n",
      "    subj 005:  FES=2.20s  NOFES=2.48s  Δ=-0.28s   |   p = 0.3581 (t = -1.59, n = 2)\n",
      "    subj 009:  FES=1.89s  NOFES=2.23s  Δ=-0.34s   |   p = 0.0667 (t = -9.50, n = 2)\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ttest_rel\n",
    "\n",
    "# Average latency per subject across sessions (collapses the pair dimension)\n",
    "def subj_cond_latencies(key):\n",
    "    \"\"\"Return {subj: {'FES': [vals...], 'NOFES': [vals...]}} using all non-null entries in `results`.\"\"\"\n",
    "    out = {s: {'FES': [], 'NOFES': []} for s in subjects}\n",
    "    for r in results:\n",
    "        v = r.get(key)\n",
    "        if v is None or (isinstance(v, float) and np.isnan(v)):\n",
    "            continue\n",
    "        out[r['subject']][r['condition']].append(v)\n",
    "    return out\n",
    "\n",
    "mi_lat   = subj_cond_latencies('mi_latency')\n",
    "rest_lat = subj_cond_latencies('rest_latency')\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 4.5), sharey=True)\n",
    "fig.suptitle('Per-subject average EARLYSTOP latency  (mean across that subject\\'s sessions ± SEM)',\n",
    "             fontsize=12, fontweight='bold', y=1.02)\n",
    "\n",
    "width = 0.38\n",
    "x     = np.arange(len(subjects))\n",
    "\n",
    "for ax, data, title in [(axes[0], mi_lat,   'MI trials'),\n",
    "                        (axes[1], rest_lat, 'REST trials')]:\n",
    "    for i, cond in enumerate(('FES', 'NOFES')):\n",
    "        means = [np.mean(data[s][cond]) if data[s][cond] else np.nan for s in subjects]\n",
    "        sems  = [np.std(data[s][cond], ddof=1) / np.sqrt(len(data[s][cond]))\n",
    "                 if len(data[s][cond]) > 1 else 0.0 for s in subjects]\n",
    "        offset = (i - 0.5) * width\n",
    "        ax.bar(x + offset, means, width, yerr=sems,\n",
    "               color=cond_color[cond], label=cond, edgecolor='white', capsize=4)\n",
    "        # Overlay individual session values\n",
    "        for xi, s in zip(x, subjects):\n",
    "            if data[s][cond]:\n",
    "                ax.scatter(np.full(len(data[s][cond]), xi + offset), data[s][cond],\n",
    "                           color='k', alpha=0.5, s=14, zorder=3)\n",
    "                           \n",
    "    # Calculate and format p-values for x-axis labels\n",
    "    new_labels = []\n",
    "    for s in subjects:\n",
    "        label = s\n",
    "        if data[s]['FES'] and data[s]['NOFES'] and len(data[s]['FES']) == len(data[s]['NOFES']) and len(data[s]['FES']) > 1:\n",
    "            try:\n",
    "                _, p_val = ttest_rel(data[s]['FES'], data[s]['NOFES'])\n",
    "                p_str = f'p={p_val:.3f}' if p_val >= 0.001 else 'p<0.001'\n",
    "                label = f'{s}\\n({p_str})'\n",
    "            except Exception:\n",
    "                pass\n",
    "        new_labels.append(label)\n",
    "                \n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(new_labels, fontsize=8) # slightly smaller to fit the p-vals\n",
    "    ax.set_xlabel('Subject')\n",
    "    ax.set_title(title, fontweight='bold')\n",
    "    ax.grid(axis='y', alpha=0.3)\n",
    "    ax.spines[['top','right']].set_visible(False)\n",
    "\n",
    "axes[0].set_ylabel('BEGIN → EARLYSTOP latency (s)')\n",
    "axes[0].legend(loc='lower right')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('per_subject_latency.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()\n",
    "print('Saved: per_subject_latency.png')\n",
    "\n",
    "# Per-subject paired Δ summary\n",
    "print('\\nPer-subject paired Δ = mean(FES lat) − mean(NOFES lat) & T-Test:')\n",
    "for key, title in [('mi_latency', 'MI'), ('rest_latency', 'REST')]:\n",
    "    data = subj_cond_latencies(key)\n",
    "    print(f'  {title} latency:')\n",
    "    for s in subjects:\n",
    "        if data[s]['FES'] and data[s]['NOFES']:\n",
    "            delta = np.mean(data[s]['FES']) - np.mean(data[s]['NOFES'])\n",
    "            \n",
    "            # Since data arrays are appended in order (Pair 1, Pair 2), we can run a paired t-test\n",
    "            # if we have balanced matched pairs (e.g. n=2 for FES and n=2 for NOFES).\n",
    "            if len(data[s]['FES']) == len(data[s]['NOFES']) and len(data[s]['FES']) > 1:\n",
    "                t_stat, p_val = ttest_rel(data[s]['FES'], data[s]['NOFES'])\n",
    "                stats_str = f'p = {p_val:.4f} (t = {t_stat:.2f}, n = {len(data[s][\"FES\"])})'\n",
    "            else:\n",
    "                stats_str = f'n = {len(data[s][\"FES\"])} (too few or unbalanced runs for paired t-test)'\n",
    "                \n",
    "            print(f'    subj {s}:  FES={np.mean(data[s][\"FES\"]):.2f}s  '\n",
    "                  f'NOFES={np.mean(data[s][\"NOFES\"]):.2f}s  Δ={delta:+.2f}s   |   {stats_str}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2ba1928b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T19:27:28.408334Z",
     "iopub.status.busy": "2026-04-22T19:27:28.408256Z",
     "iopub.status.idle": "2026-04-22T19:27:28.518108Z",
     "shell.execute_reply": "2026-04-22T19:27:28.517764Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: grand_avg_latency.png\n",
      "\n",
      "Grand-average latency (across-subject mean ± SEM) & Paired T-Test:\n",
      "  MI     FES = 2.523 ± 0.032 s   NOFES = 2.235 ± 0.084 s   Δ = +0.288 s   |   p = 0.0632 (t = 2.89, n = 4)\n",
      "  REST   FES = 2.135 ± 0.094 s   NOFES = 2.391 ± 0.102 s   Δ = -0.256 s   |   p = 0.0163 (t = -4.89, n = 4)\n"
     ]
    }
   ],
   "source": [
    "# Grand average across subjects: one pair of bars per class (MI, REST).\n",
    "# Each subject contributes one mean (averaged across their sessions) per condition,\n",
    "# so error bars = between-subject SEM.\n",
    "from scipy.stats import ttest_rel\n",
    "\n",
    "def subject_means(data):\n",
    "    return {cond: np.array([np.mean(data[s][cond]) for s in subjects if data[s][cond]])\n",
    "            for cond in ('FES', 'NOFES')}\n",
    "\n",
    "mi_sub   = subject_means(mi_lat)\n",
    "rest_sub = subject_means(rest_lat)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 4.5))\n",
    "classes = ['MI', 'REST']\n",
    "x       = np.arange(len(classes))\n",
    "width   = 0.38\n",
    "\n",
    "p_values = {}\n",
    "for cls, d in [('MI', mi_sub), ('REST', rest_sub)]:\n",
    "    subjects_with_both = [s for s in subjects if mi_lat[s]['FES'] and mi_lat[s]['NOFES']] if cls == 'MI' else [s for s in subjects if rest_lat[s]['FES'] and rest_lat[s]['NOFES']]\n",
    "    fes_paired = np.array([np.mean(mi_lat[s]['FES']) if cls == 'MI' else np.mean(rest_lat[s]['FES']) for s in subjects_with_both])\n",
    "    nofes_paired = np.array([np.mean(mi_lat[s]['NOFES']) if cls == 'MI' else np.mean(rest_lat[s]['NOFES']) for s in subjects_with_both])\n",
    "    \n",
    "    if len(fes_paired) > 1:\n",
    "        _, p_val = ttest_rel(fes_paired, nofes_paired)\n",
    "        p_values[cls] = p_val\n",
    "    else:\n",
    "        p_values[cls] = np.nan\n",
    "\n",
    "for i, cond in enumerate(('FES', 'NOFES')):\n",
    "    means = [mi_sub[cond].mean(),        rest_sub[cond].mean()]\n",
    "    sems  = [mi_sub[cond].std(ddof=1) / np.sqrt(len(mi_sub[cond])),\n",
    "             rest_sub[cond].std(ddof=1) / np.sqrt(len(rest_sub[cond]))]\n",
    "    offset = (i - 0.5) * width\n",
    "    ax.bar(x + offset, means, width, yerr=sems,\n",
    "           color=cond_color[cond], label=cond, edgecolor='white', capsize=5)\n",
    "    for j, arr in enumerate([mi_sub[cond], rest_sub[cond]]):\n",
    "        ax.scatter(np.full(len(arr), x[j] + offset), arr,\n",
    "                   color='k', alpha=0.6, s=20, zorder=3)\n",
    "\n",
    "# Modify X-labels to contain the P-Values below each\n",
    "new_labels = []\n",
    "for cls in classes:\n",
    "    p_val = p_values[cls]\n",
    "    if not np.isnan(p_val):\n",
    "        p_str = f'p={p_val:.3f}' if p_val >= 0.001 else 'p<0.001'\n",
    "        new_labels.append(f'{cls}\\n({p_str})')\n",
    "    else:\n",
    "        new_labels.append(cls)\n",
    "\n",
    "ax.set_xticks(x); ax.set_xticklabels(new_labels)\n",
    "ax.set_ylabel('BEGIN → EARLYSTOP latency (s)')\n",
    "ax.set_title(f'Grand-average EARLYSTOP latency across {len(subjects)} subjects\\n'\n",
    "             '(points = per-subject means, bars = across-subject mean ± SEM)',\n",
    "             fontweight='bold')\n",
    "ax.legend(); ax.grid(axis='y', alpha=0.3)\n",
    "ax.spines[['top','right']].set_visible(False)\n",
    "plt.tight_layout()\n",
    "plt.savefig('grand_avg_latency.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()\n",
    "print('Saved: grand_avg_latency.png')\n",
    "\n",
    "print('\\nGrand-average latency (across-subject mean ± SEM) & Paired T-Test:')\n",
    "sem = lambda a: a.std(ddof=1) / np.sqrt(len(a)) if len(a) > 1 else 0.0\n",
    "for cls, d in [('MI', mi_sub), ('REST', rest_sub)]:\n",
    "    # Ensure arrays are aligned for t-test by taking paired subjects only\n",
    "    subjects_with_both = [s for s in subjects if mi_lat[s]['FES'] and mi_lat[s]['NOFES']] if cls == 'MI' else [s for s in subjects if rest_lat[s]['FES'] and rest_lat[s]['NOFES']]\n",
    "    \n",
    "    fes_paired = np.array([np.mean(mi_lat[s]['FES']) if cls == 'MI' else np.mean(rest_lat[s]['FES']) for s in subjects_with_both])\n",
    "    nofes_paired = np.array([np.mean(mi_lat[s]['NOFES']) if cls == 'MI' else np.mean(rest_lat[s]['NOFES']) for s in subjects_with_both])\n",
    "    \n",
    "    delta = d['FES'].mean() - d['NOFES'].mean()\n",
    "    \n",
    "    if len(fes_paired) > 1:\n",
    "        t_stat, p_val = ttest_rel(fes_paired, nofes_paired)\n",
    "        stats_str = f'p = {p_val:.4f} (t = {t_stat:.2f}, n = {len(fes_paired)})'\n",
    "    else:\n",
    "        stats_str = 'Not enough paired data for t-test'\n",
    "        \n",
    "    print(f'  {cls:<5}  FES = {d[\"FES\"].mean():.3f} ± {sem(d[\"FES\"]):.3f} s   '\n",
    "          f'NOFES = {d[\"NOFES\"].mean():.3f} ± {sem(d[\"NOFES\"]):.3f} s   '\n",
    "          f'Δ = {delta:+.3f} s   |   {stats_str}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21da9cda",
   "metadata": {},
   "source": [
    "---\n",
    "## Figure 6 — Decision-margin distribution shift (Wasserstein-1)\n",
    "\n",
    "Mean |margin| collapses the whole distribution to a scalar. If FES spreads the margin\n",
    "distribution further from zero (or sharpens it into a narrow high-confidence mode) without\n",
    "moving the mean, that shows up here but not in mean amplitude. For each (subject × pair),\n",
    "we compute W₁ between the FES and NOFES margin distributions, separately for MI and REST\n",
    "trials. Larger W₁ = greater distributional divergence between conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "41bf28d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T19:27:28.519611Z",
     "iopub.status.busy": "2026-04-22T19:27:28.519516Z",
     "iopub.status.idle": "2026-04-22T19:27:28.702716Z",
     "shell.execute_reply": "2026-04-22T19:27:28.702337Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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B7+k3TMd9oqn5tK90DeLp2rWrHXHEEa7cxdo/yfyO6lypc6T/90Aps/RZXQNoeu/8kJ1HH300+LxJkybWo0cPN7i0zuMqy7quiJfOhRo41p/aUcupc47GfVAaHf3u6LVo53Jdd+g3SNccurbT8aNjQzR+hc6tyaZN1Hla21bndJ0HlE5KJk6c6P5qv+u36oUXXgj+Rmgf6De2Vq1aKTvuNZi3zv0a60PbXGOglC9f3v2+XHLJJcGyqRRoKlc6N+m49F9XJku/t7oG1nfrfKJ19cqUxgjROSU3y3s0c+bMsZ9++im4jZW2TNtGv/36zfWfZxL53Q2n+SjdqH6ntF29NKaJ0O+HrrOV/k7r6qWn0/lc14Uqp8lK5Bo8Fm0/lUOPxlPR+DpaX+/coOVWOYh2fR5vWUlk3wEAYogRPAAABAKBI444IqS1izz77LPB15RSR2kxvP8ffPDBLK2p1LI1UutvdatXl/IHHnjAdeM/44wzgp9Rt2F1I/bSI3ivq7VneJdzUZoIT6LTq2uuv4V/ePohpZzxt/D1pz3wt9DRe19//XWW7/Kvl7pFh1Nr2vAULrFao3ktBZXSwJvm0ksvDXn/7bffDlk2bZNEexCotaz2k0et7rz31DIqP3sQKCWBulF7n1dXc3/rabWOTbQHgVp7+l9XypNwajWq9FWeSN3nYy3z559/7qZ56KGHXJlXGgr/5z/55JOo20gpI7wUD0qz4O+tovl4rrvuupDPRUqntXTp0oi9G9SzwH+86HvUrT38+E60ZfGUKVNC5ulv+di0adOI5xu10PentFAKpGg9muIpT0rN45/GS2ej1sH63+vZ5J3nlOLEP/3KlStzdL5QzwD/+c2bn2g91VrQe1/njJyWg+zE0wMmvNVh+LET7RHeAl3b2p9yLvyhddB6+bdJvHTuVFlQ2qz77rvPHVf+1pRqYR9tvdVS+Ntvvw2+p3Ra/uXyzn86F/hbFat3iNdbTtQCOt7zgF/4+UPbTSlYvP/POuusmD0I1Krd/7pXdiMJP78p9URO92v4ckV7hB+P1157bczplYZDv5XhvYuiUUoy/+f79u0bfE/Hino6RjrnJ/s7Gt67UilAvOsVz++//x7yWrTtqHOu9/qLL76YZd02bdoU0nsh2u+X1kW9A7zXlRbP/1vlv27TQ9ddka439Jg5c2bwPf1W+d9Tap54hZdvlTlvm+j6z/+ejlnvvfDt/r///S+lx33474dH53L/NE888UTwPZ3Hw1vsJ9ODQD0X/OnglG7TX+5zs7zHavWu7eG/Zo+UTsp/3RDv7254GdA1uY6NcIn0IPD3ftJ1X6NGjYLv6brQS1mVTA+CeK/BY21LHSP+15Xix0/7y/++/zcombKSzL4DAGRFDwIAyIZa+n/zzTfB1jlqseT1DlArM7X6UysrtYhXC1m9p5Y2avniUS8Dj1q+3Hrrra6Fj1rZRqP31NJNrcbUMm+//fZzLfrU0lUD3aqlVMOGDd176p2gFmWeRKf/6quvgi385cYbb3SPaC02NX23bt2yvKfWg/qecFr/119/3T3Xuut548aN3fJoem1j9SxIhFreaf08au0Uq0W/WhCpdWYi1ApJ+9WjZVbLO1HLJI9aUb7zzjtZPq8WZLk1cJ9aS6u8efQ9pUuXDmldp5ZkiVDr25YtWwZ7JTRr1sy1UlQrNZUXtW5Ui+dkqPWhWoavWbMm5nRez5xIdFyp5a3Xgk+t8b3W6P794W8ZrOkitaL0Dzzr78GiHkD+7RipHKlVcyLU2lSt/fzLpJb0amkvavmpHgJaN3+LO7WyU2u4WMuinhLxUitAnQu8wXK13nrNa3mn9dLAsOHnOVErbq9FZLLnC/921vlNvYxirVtOy0E6US8BtZy84447XM8wDeAevp1mzJjh9r/2h1pwZkfrOmDAAPvf//4XbPGb6DGl1sz+Y1qtLsO/Q+dAtZT1f4fOjf6yqZb/XivonFJLf7Vq1+/ua6+9llAL8oJCvRPUul090SINTq0eR8OGDXPXAJF6MoQLb4Xs7/mhY6Vv37724Ycfpux3NLz3hcp1eKv6WMd3+PWBzrteOdL369pAv7c6bnS94vW+iEXrou3lUe8kf280lVn15PDOXeohqJ4z4WrXru16GMQ6JjzR9o2/x4ifekd420nn4mjvhfea8X9nKo57lb1Ivx3+cqTjW9/j0XlW20U9MXJC89A2jrR94z1/J1veY1GrePUYUW8RXV/pGkFlXX+1vXSdGmvA+nhp2eI9NqLxr6+uV3TOvPPOO93/ui5csmSJ662ZX8J/v/3lSAYOHOh6xnh0LEa6toy3rOTVvgOAwo4AAQDEcfOqygqvcl+VXF7FmYIDXmWi0mGokljvq0LDXwHkDxA89thjNmrUqLi2uxdA0HdMnz7d3Tyry+/q1auD6Yc86rKuinddJCc6vSojEuGvUPDzBx38lD7l559/DnbxV4WhHh5181dqG6XyiVeqljmW8Bt4f8Wxl47Gu1m9/vrrs3xe+z23AgT+rvVeupZY/8dLqYnUnVuVVtrG7777bsj7SpWi1xSAipeCKpqnf5tFEytoFu/+8JcNpe6IVckePn1ulCOlE1A6h1j7x9ufsSp8UrEsSs0zYcIE91yVfAoQ6Dt1HlAKGVX06Rj1n+e8z+X02Evkc5pW+zTSvou3HCRC5cQLnCRC6aBGjBgR17RKwfTKK6+43wZVfuu8oUos/W54aX2Uoknnbn8FUDQXXnhhMPCaG8eUf3vm1vkmElXwKX2EV+aUbi9a5Y6OLaWjUYoVibUPw9/zVzzlZL/6KTVS+DaNRr/Peii4pd9DXTcoXYh+Kz1KP3b33XfHDFrmZP+k6liuX7++JUvrp3KvCm+VVZ2X/AEIpdHRcRMt5ZgnfJn0OT815FAlt9LoRJo+0WNCIv3ui1KuRQoQ+BtCqNxGK49a1mjfmYrjPtq1mr8cKfgavozh2zQZsbZvvL9/uXE+0r557rnn7LLLLnNlRMeyHv7zktJsKYVVdtcUsUTb9onIbn2jBVrCt2+sMpIT2R2L4f8ncyz61yWv9h0AFHYECAAgG8qzrAtK7wZNrV68igb/uAJqoaKKnnXr1rmbej9/gEAtqf03hKoMUssZXfiOGzfOXeBGWw7liVXrXrW2U35aPVeFgiqXlBtWOVG9VuOJTK8bQT/l3FbrvWgi5ZgX5bCNRJWjynGsSh9VhKiln5ZNFc1aLuU4VoWYckcrf3E8wpdZN+QK2MRqKZuo8BaRXqvldKDW/n5eC+po/8dLrc5Uoa99pNbM2k+qsFKFhFpn6TW1FvePVZAdVe54x4+2oW7k1IJeLZM1b/VUSOX+8JcNVTxFq2j2T+/1bNCx6G/tH075cBOlc4KX7z/a/tH+1Pr4c5MrF7V65USTTAtBf4BArfy8ij3lBlagTi3xlH999uzZrlWf/3OeZM8X/s+p8ky9FWKJtn/T+biMh1o1qzeOHuq1od8Anb/8ZTY7GktDFaoe5WJWzn7tT5Wznj17uuMuO/Fuy9w630TjlX39pqqcermww2ldVXGs8upVHCqftfJoh/PnCg///c5PqtxTS1k91HhAf70c6gomadtm1+I40v7xn1ej7Z9kf0fDP6fKOI2Bkwz1ltHYIV6gRGMD6KFtoMYNqvBTq+toZSDauniBAI96Dvh7GIRPnxfnl1hjF4QHBXLzuI92reYvR6pk1jbzL1f4Nk1GKrZvsuU9O2qkouNPAVyNo6HrU13zzJo1y/0uq9efxlJSeUxWtG2fiPBzQqTrCQm/7vHG0hD1Lk71eTvWsejfP+HlKBXHYl7sOwAo7AgQAEA2dKGtG18vtYwqcyJV/Puf+6dRV3l/yzD/DarS66hiTlSJGe3GTq0j1WVYXWW1LP4bcQ1K61UmeN2uE51ey6CbQK/rvVoVRUrLosoXVdioJWwiVNmsilUN3qZKH6/SUxfvXkoiDSirVCve//4bA90Uh1NXY7UG9LanWiCpsi38Jls3RBoUVpVxucVrCZqX1ArNS2vlVX5p0GCvhVWyaQC0T1RJru3r786tXiDeYJL+7v3hN3CR9pW/zKsSWt3hvRtXf8AsVVTxN2/evGC5ePDBB92guuEVWl7luCoYlcpENHC2WpqFt3DT8anW3jqeE6UW+RpcTykuvGXyD6KotF9epYG2vZdmyBswMjzdjOanz/uDXvHsB6/i1b9fvDRHXmWp/ipAoHQxqkDwbsr9A0cme77QdtaxKJq3jvVIA1IqxY4+W9Aq/mO5/PLL3cDPWt/wSht/GjOvVXx2tH28XgeiFE5e2VSwS4M2ppL2lT94pXKjc563LjlNOxKJAsqq3NGxp+MyVsopL0DgtepWajR/JaKC4+q95w/SKM1FflAFlq4RFFgLb3mvbew/3vV/pEGPw4UPGqr94R3r2mf+dB6p+B3VeUL7x6PeFrp+8X/uzz//dIOwZjegr453pdTxAiX+QU01gLN3vlagNdaxEb4u6g2n30QvzZDKrD81WiquCRLp8ZUKuX3c67jRQMei79Hvs34PRds1np4LeSHZ8p7dtlX5V686bQc9PEq9qEpn7/rHq2SO93c31bS+XoBdv7/+6yj9nnjXbuGBFAXgvIYFCkbGKr/ZXYPHEn5s6XrC38ggvIFJTo/FZPYdACArAgQAEAdV/nsBAu+CWhfP/go6VaDoxl4V3f6Lbn/gQHThrtZx8tZbb7lKAnWP1XPleY5ElWmqZNNNdLt27dy4BGqBq/koT3l4K5xEp1cFhJbDyz2smytV1qu1jW42dOOp9VfFoYIdyu2bCFUsq9WyKsd0Aa/KV93UeJWyHn9FiL+ltipUVEHttZhSLwulRFFFkJf7XBVEWmfdMKsSQTcLqhRSqgJVbsSTsiO3afuqVZM3boHfPffcE0xJoNavkVrA+qkiRnldH3nkEfe/AkKquFXLfLW0VIV0MlTxo/2gcqt9refKZayu2ZFae6kCyJ/iQzmZVZGgniBKC6JKUX+gQTdyChCph4v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      "text/plain": [
       "<Figure size 1560x540 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: margin_wasserstein.png\n",
      "\n",
      "Mean W₁ across 7 (subject × pair) comparisons:\n",
      "  MI trials:   1.207 ± 1.150\n",
      "  REST trials: 0.785 ± 0.623\n",
      "  W₁(MI) > W₁(REST) in 6/7 comparisons — FES perturbs MI margins more than REST margins\n"
     ]
    }
   ],
   "source": [
    "w_results = []   # one row per (subject, pair) — Wasserstein between FES and NOFES margin dists\n",
    "for subj in subjects:\n",
    "    for pair in PAIRS:\n",
    "        fes = next((r for r in results if r['subject']==subj and r['pair']==pair['name']\n",
    "                    and r['condition']=='FES'),   None)\n",
    "        nof = next((r for r in results if r['subject']==subj and r['pair']==pair['name']\n",
    "                    and r['condition']=='NOFES'), None)\n",
    "        if fes is None or nof is None: continue\n",
    "        if fes['margin'].size == 0 or nof['margin'].size == 0: continue\n",
    "\n",
    "        def w1(fe_mask, no_mask):\n",
    "            f = fes['margin'][fe_mask];  n = nof['margin'][no_mask]\n",
    "            return wasserstein_distance(f, n) if (f.size and n.size) else np.nan\n",
    "\n",
    "        w_results.append(dict(\n",
    "            subject=subj, pair=pair['name'].split()[0],\n",
    "            w_mi   = w1(fes['y_test'] == 1, nof['y_test'] == 1),\n",
    "            w_rest = w1(fes['y_test'] == 0, nof['y_test'] == 0),\n",
    "            w_all  = wasserstein_distance(fes['margin'], nof['margin']),\n",
    "            fes_mi_mean   = fes['margin'][fes['y_test']==1].mean() if (fes['y_test']==1).any() else np.nan,\n",
    "            nofes_mi_mean = nof['margin'][nof['y_test']==1].mean() if (nof['y_test']==1).any() else np.nan,\n",
    "        ))\n",
    "\n",
    "if w_results:\n",
    "    labels  = [f'{r[\"subject\"]}\\n{r[\"pair\"]}' for r in w_results]\n",
    "    w_mi    = np.array([r['w_mi']   for r in w_results])\n",
    "    w_rest  = np.array([r['w_rest'] for r in w_results])\n",
    "    x = np.arange(len(labels))\n",
    "\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(13, 4.5))\n",
    "    fig.suptitle('Wasserstein-1 distance between FES and NOFES decision-margin distributions',\n",
    "                 fontsize=12, fontweight='bold', y=1.02)\n",
    "\n",
    "    width = 0.4\n",
    "    axes[0].bar(x - width/2, w_mi,   width, color='#E05C2A', label='MI trials',   edgecolor='white')\n",
    "    axes[0].bar(x + width/2, w_rest, width, color='#2A7BE0', label='REST trials', edgecolor='white')\n",
    "    axes[0].set_xticks(x); axes[0].set_xticklabels(labels, fontsize=8)\n",
    "    axes[0].set_ylabel('W₁(FES margin, NOFES margin)')\n",
    "    axes[0].set_title('Per-(subject × pair), by trial class', fontweight='bold')\n",
    "    axes[0].legend(); axes[0].grid(axis='y', alpha=0.3)\n",
    "    axes[0].spines[['top','right']].set_visible(False)\n",
    "\n",
    "    # Direction of the shift (FES mean − NOFES mean) on MI margins\n",
    "    fes_mi    = np.array([r['fes_mi_mean']   for r in w_results])\n",
    "    nofes_mi  = np.array([r['nofes_mi_mean'] for r in w_results])\n",
    "    mi_shift  = fes_mi - nofes_mi\n",
    "    colors_sh = ['#E05C2A' if d > 0 else '#2A7BE0' for d in mi_shift]\n",
    "    axes[1].bar(x, mi_shift, color=colors_sh, edgecolor='white')\n",
    "    axes[1].axhline(0, color='k', lw=0.8)\n",
    "    axes[1].set_xticks(x); axes[1].set_xticklabels(labels, fontsize=8)\n",
    "    axes[1].set_ylabel('mean(FES MI-margin) − mean(NOFES MI-margin)')\n",
    "    axes[1].set_title('Direction of MI-margin shift (positive = FES margin more negative is\\ncolumn below zero; see sign convention in code)', fontsize=9, fontweight='bold')\n",
    "    axes[1].grid(axis='y', alpha=0.3)\n",
    "    axes[1].spines[['top','right']].set_visible(False)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('margin_wasserstein.png', dpi=150, bbox_inches='tight')\n",
    "    plt.show()\n",
    "    print('Saved: margin_wasserstein.png')\n",
    "\n",
    "    print(f'\\nMean W₁ across {len(w_results)} (subject × pair) comparisons:')\n",
    "    print(f'  MI trials:   {np.nanmean(w_mi):.3f} ± {np.nanstd(w_mi, ddof=1):.3f}')\n",
    "    print(f'  REST trials: {np.nanmean(w_rest):.3f} ± {np.nanstd(w_rest, ddof=1):.3f}')\n",
    "    print(f'  W₁(MI) > W₁(REST) in {int((w_mi > w_rest).sum())}/{len(w_results)} comparisons '\n",
    "          f'— FES perturbs MI margins more than REST margins')\n",
    "else:\n",
    "    print('No comparisons available for Wasserstein analysis.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3db60ba",
   "metadata": {},
   "source": [
    "---\n",
    "## Summary Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f27a80e",
   "metadata": {},
   "source": [
    "---\n",
    "## Figure 4 — Within-session MI-accuracy trajectory (sliding window)\n",
    "\n",
    "Refined from the original \"thirds\" analysis to address its noise floor. Each online session\n",
    "is parsed into chronological MI-only trials, then a W-trial sliding window gives a smoother\n",
    "accuracy trajectory. The slope of a linear fit to this trajectory is a per-session \"learning\n",
    "rate.\" Restricting to MI trials isolates the class that FES actually perturbs (orthotic +\n",
    "stimulation fires on MI, not REST) — slope differences between FES and NOFES sessions would\n",
    "indicate whether proprioceptive feedback accelerates within-session adaptation.\n",
    "\n",
    "Caveat: even with sliding windows, ~25–30 MI trials per session is low-N and the slope\n",
    "estimator is noisy; read this as exploratory, not confirmatory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a9db789c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T19:27:28.705422Z",
     "iopub.status.busy": "2026-04-22T19:27:28.705328Z",
     "iopub.status.idle": "2026-04-22T19:27:28.901376Z",
     "shell.execute_reply": "2026-04-22T19:27:28.900874Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1560x540 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: within_session_mi_trajectory.png\n",
      "\n",
      "Condition-averaged slopes (FES − NOFES paired per session):\n",
      "  FES:   mean slope = +0.0020  (n=8)\n",
      "  NOFES: mean slope = +0.0045  (n=8)\n"
     ]
    }
   ],
   "source": [
    "WINDOW = 8   # trials per sliding window\n",
    "\n",
    "lr_results = []\n",
    "for r in results:\n",
    "    mi_successes = np.array([int(t['success']) for t in r['trials'] if t['cls'] == 'MI'])\n",
    "    if len(mi_successes) < WINDOW + 2:\n",
    "        continue\n",
    "    traj = np.array([mi_successes[i:i+WINDOW].mean()\n",
    "                     for i in range(len(mi_successes) - WINDOW + 1)])\n",
    "    slope = np.polyfit(np.arange(len(traj)), traj, 1)[0]\n",
    "    lr_results.append(dict(subj=r['subject'], pair=r['pair'], cond=r['condition'],\n",
    "                           traj=traj, slope=slope, n_mi=len(mi_successes)))\n",
    "\n",
    "# ── Figure 4a: slope per condition (FES vs NOFES) ─────────────────────────────\n",
    "fig, axes = plt.subplots(1, 2, figsize=(13, 4.5))\n",
    "fig.suptitle(f'MI-only within-session learning rate  (window = {WINDOW} trials)',\n",
    "             fontsize=12, fontweight='bold', y=1.02)\n",
    "\n",
    "fes_slopes   = np.array([r['slope'] for r in lr_results if r['cond'] == 'FES'])\n",
    "nofes_slopes = np.array([r['slope'] for r in lr_results if r['cond'] == 'NOFES'])\n",
    "\n",
    "ax = axes[0]\n",
    "means  = [fes_slopes.mean(), nofes_slopes.mean()]\n",
    "sems   = [fes_slopes.std(ddof=1) / np.sqrt(len(fes_slopes)),\n",
    "          nofes_slopes.std(ddof=1) / np.sqrt(len(nofes_slopes))]\n",
    "ax.bar(['FES', 'NOFES'], means, yerr=sems,\n",
    "       color=[cond_color['FES'], cond_color['NOFES']],\n",
    "       capsize=6, edgecolor='white')\n",
    "# Overlay individual points\n",
    "for i, slopes in enumerate([fes_slopes, nofes_slopes]):\n",
    "    ax.scatter(np.full(len(slopes), i) + np.random.uniform(-0.08, 0.08, len(slopes)),\n",
    "               slopes, color='k', alpha=0.5, s=20, zorder=3)\n",
    "ax.axhline(0, color='k', linestyle='--', lw=0.8)\n",
    "ax.set_ylabel('Slope of MI-accuracy trajectory\\n(Δ fraction correct / trial-step)')\n",
    "ax.set_title('Condition-averaged learning rate', fontweight='bold')\n",
    "ax.spines[['top','right']].set_visible(False)\n",
    "ax.grid(axis='y', alpha=0.3)\n",
    "\n",
    "# ── Figure 4b: trajectories per (subject × pair) ─────────────────────────────\n",
    "ax = axes[1]\n",
    "for r in lr_results:\n",
    "    ax.plot(r['traj'], color=cond_color[r['cond']], alpha=0.6, lw=1.5,\n",
    "            label=r['cond'])\n",
    "ax.axhline(0.5, color='gray', linestyle='--', lw=0.8, alpha=0.6)\n",
    "ax.set_xlabel(f'Sliding window start (MI trial index, window = {WINDOW})')\n",
    "ax.set_ylabel('MI accuracy within window')\n",
    "ax.set_title('All session trajectories overlaid', fontweight='bold')\n",
    "# Dedup legend\n",
    "h, l = ax.get_legend_handles_labels()\n",
    "seen = set()\n",
    "handles = [(hh, ll) for hh, ll in zip(h, l) if not (ll in seen or seen.add(ll))]\n",
    "ax.legend([h for h, _ in handles], [l for _, l in handles], loc='lower left')\n",
    "ax.spines[['top','right']].set_visible(False)\n",
    "ax.grid(axis='y', alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('within_session_mi_trajectory.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()\n",
    "print('Saved: within_session_mi_trajectory.png')\n",
    "print(f'\\nCondition-averaged slopes (FES − NOFES paired per session):')\n",
    "print(f'  FES:   mean slope = {fes_slopes.mean():+.4f}  (n={len(fes_slopes)})')\n",
    "print(f'  NOFES: mean slope = {nofes_slopes.mean():+.4f}  (n={len(nofes_slopes)})')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ddedb148",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1680x1080 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: avg_within_session_trajectory_cross_overlay.png\n"
     ]
    }
   ],
   "source": [
    "# Re-calculate trajectories for BOTH MI and REST trials to support the requested 2x2 overlay\n",
    "WINDOW_BOTH = 8\n",
    "\n",
    "lr_results_all = []\n",
    "for r in results:\n",
    "    for trial_type in ['MI', 'REST']:\n",
    "        successes = np.array([int(t['success']) for t in r['trials'] if t['cls'] == trial_type])\n",
    "        if len(successes) < WINDOW_BOTH + 2:\n",
    "            continue\n",
    "        traj = np.array([successes[i:i+WINDOW_BOTH].mean()\n",
    "                         for i in range(len(successes) - WINDOW_BOTH + 1)])\n",
    "        lr_results_all.append(dict(\n",
    "            subj=r['subject'], pair=r['pair'], cond=r['condition'], \n",
    "            trial_type=trial_type, traj=traj\n",
    "        ))\n",
    "\n",
    "# Create a 2x2 grid: Rows = MI vs REST, Cols = ONLINE FES vs ONLINE NOFES\n",
    "# In each subplot, overlay Pair 1 (FES CALIB) vs Pair 2 (NOFES CALIB)\n",
    "fig, axes = plt.subplots(2, 2, figsize=(14, 9), sharey=True, sharex=True)\n",
    "fig.suptitle(f'Cross-Subject Average Trajectory split by Online Condition & Trial Type (window = {WINDOW_BOTH})\\n'\n",
    "             'Lines overlay Calibration Types: Pair 1 (FES) vs Pair 2 (NOFES)',\n",
    "             fontsize=13, fontweight='bold', y=1.02)\n",
    "\n",
    "TRIAL_TYPES = ['MI', 'REST']\n",
    "ONLINE_CONDS = ['FES', 'NOFES']\n",
    "\n",
    "# Define distinct line colors / styles for the Calibration pairs to avoid confusion with Online Conds\n",
    "# Pair 1 (FES_CALIB) -> dark orange-ish\n",
    "# Pair 2 (NOFES_CALIB) -> dark blue-ish\n",
    "calib_style = {\n",
    "    'Pair1': {'color': '#d95f02', 'label': 'FES_CALIB (Pair 1)'},\n",
    "    'Pair2': {'color': '#1f78b4', 'label': 'NOFES_CALIB (Pair 2)'}\n",
    "}\n",
    "\n",
    "import warnings\n",
    "\n",
    "for row_idx, trial_type in enumerate(TRIAL_TYPES):\n",
    "    for col_idx, online_cond in enumerate(ONLINE_CONDS):\n",
    "        ax = axes[row_idx, col_idx]\n",
    "        \n",
    "        for pair_prefix, style_info in calib_style.items():\n",
    "            # Extract trajectories matching this specific combination:\n",
    "            # (trial_type, online_cond, pair starts with Pair1 or Pair2)\n",
    "            match_subset = [r['traj'] for r in lr_results_all \n",
    "                            if r['trial_type'] == trial_type \n",
    "                            and r['cond'] == online_cond \n",
    "                            and r['pair'].startswith(pair_prefix)]\n",
    "            \n",
    "            if not match_subset:\n",
    "                continue\n",
    "                \n",
    "            max_len = max(len(t) for t in match_subset)\n",
    "            padded = np.full((len(match_subset), max_len), np.nan)\n",
    "            for j, t in enumerate(match_subset):\n",
    "                padded[j, :len(t)] = t\n",
    "                \n",
    "            with warnings.catch_warnings():\n",
    "                warnings.simplefilter(\"ignore\", category=RuntimeWarning)\n",
    "                mean_traj = np.nanmean(padded, axis=0)\n",
    "                n_present = np.sum(~np.isnan(padded), axis=0)\n",
    "                sem_traj = np.nanstd(padded, axis=0, ddof=1) / np.sqrt(n_present)\n",
    "                \n",
    "            x_vals = np.arange(max_len)\n",
    "            ax.plot(x_vals, mean_traj, color=style_info['color'], label=style_info['label'], lw=2)\n",
    "            ax.fill_between(x_vals, mean_traj - sem_traj, mean_traj + sem_traj, \n",
    "                            color=style_info['color'], alpha=0.15, edgecolor='none')\n",
    "            \n",
    "        ax.axhline(0.5, color='gray', linestyle='--', lw=0.8, alpha=0.6)\n",
    "        ax.set_title(f'ONLINE: {online_cond} | Trials: {trial_type}', fontweight='bold')\n",
    "        ax.grid(axis='y', alpha=0.3)\n",
    "        ax.spines[['top','right']].set_visible(False)\n",
    "        \n",
    "        if row_idx == 1:\n",
    "            ax.set_xlabel('Sliding window start (trial index)')\n",
    "        if col_idx == 0:\n",
    "            ax.set_ylabel('Average Accuracy within window')\n",
    "        if row_idx == 0 and col_idx == 0:\n",
    "            ax.legend(loc='lower left')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('avg_within_session_trajectory_cross_overlay.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()\n",
    "print('Saved: avg_within_session_trajectory_cross_overlay.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cf55268e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T19:27:28.903912Z",
     "iopub.status.busy": "2026-04-22T19:27:28.903829Z",
     "iopub.status.idle": "2026-04-22T19:27:28.914870Z",
     "shell.execute_reply": "2026-04-22T19:27:28.914546Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Aggregate across 8 complete (subject × pair) comparisons ===\n",
      "\n",
      "Metric                                      FES              NOFES  paired Δ       95% CI (bootstrap)       sign\n",
      "----------------------------------------------------------------------------------------------------------------\n",
      "Overall accuracy (markers)       0.821 ±  0.077     0.806 ±  0.075    +0.015 [ -0.040, +0.065]    4/7\n",
      "MI accuracy                      0.817 ±  0.108     0.775 ±  0.115    +0.042 [ -0.079, +0.142]    5/7\n",
      "REST accuracy                    0.825 ±  0.100     0.837 ±  0.061    -0.012 [ -0.070, +0.039]    4/7\n",
      "MI EARLYSTOP latency (s)         2.523 ±  0.204     2.235 ±  0.209    +0.288 [ +0.058, +0.530] *  6/8\n",
      "REST EARLYSTOP latency (s)       2.135 ±  0.303     2.391 ±  0.270    -0.256 [ -0.416, -0.077] *  2/8\n",
      "Classification amplitude         2.237 ±  2.063     2.362 ±  1.813    +0.151 [ -0.142, +0.476]    4/7\n",
      "Fisher ratio (test SNR)          2.307 ±  2.712     2.542 ±  3.263    +0.090 [ -1.065, +1.225]    3/7\n",
      "μ-band SNR (REST/MI)             1.751 ±  0.671     1.747 ±  0.480    +0.065 [ -0.161, +0.299]    4/7\n",
      "\n",
      "* = 95% bootstrap CI excludes zero (suggestive at this n).\n",
      "Sign column: number of (subject × pair) comparisons where FES > NOFES.\n"
     ]
    }
   ],
   "source": [
    "def boot_paired_ci(deltas, n_boot=10000, alpha=0.05, seed=0):\n",
    "    \"\"\"Bootstrap percentile CI on the mean of paired differences.\"\"\"\n",
    "    rng = np.random.default_rng(seed)\n",
    "    d = np.asarray([x for x in deltas if x is not None and not (isinstance(x, float) and np.isnan(x))])\n",
    "    if len(d) == 0:\n",
    "        return (np.nan, np.nan, np.nan)\n",
    "    boot_means = d[rng.integers(0, len(d), size=(n_boot, len(d)))].mean(axis=1)\n",
    "    lo, hi = np.percentile(boot_means, [100*alpha/2, 100*(1-alpha/2)])\n",
    "    return float(d.mean()), float(lo), float(hi)\n",
    "\n",
    "\n",
    "# Collect all (subject × pair) where both FES and NOFES exist\n",
    "paired = []\n",
    "for subj in subjects:\n",
    "    for pair in PAIRS:\n",
    "        fes = next((r for r in results if r['subject']==subj and r['pair']==pair['name']\n",
    "                    and r['condition']=='FES'),   None)\n",
    "        nof = next((r for r in results if r['subject']==subj and r['pair']==pair['name']\n",
    "                    and r['condition']=='NOFES'), None)\n",
    "        if fes and nof: paired.append((fes, nof))\n",
    "\n",
    "print(f'=== Aggregate across {len(paired)} complete (subject × pair) comparisons ===\\n')\n",
    "\n",
    "METRICS_SUMMARY = [\n",
    "    ('acc',          'Overall accuracy (markers)'),\n",
    "    ('mi_acc',       'MI accuracy'),\n",
    "    ('rest_acc',     'REST accuracy'),\n",
    "    ('mi_latency',   'MI EARLYSTOP latency (s)'),\n",
    "    ('rest_latency', 'REST EARLYSTOP latency (s)'),\n",
    "    ('amp',          'Classification amplitude'),\n",
    "    ('fisher',       'Fisher ratio (test SNR)'),\n",
    "    ('mu_snr',       'μ-band SNR (REST/MI)'),\n",
    "]\n",
    "\n",
    "hdr = (f'{\"Metric\":<28} {\"FES\":>18} {\"NOFES\":>18} '\n",
    "       f'{\"paired Δ\":>9} {\"95% CI (bootstrap)\":>24} {\"sign\":>10}')\n",
    "print(hdr); print('-' * len(hdr))\n",
    "\n",
    "def fmt_mean_sd(arr):\n",
    "    a = np.array([x for x in arr if x is not None and not (isinstance(x, float) and np.isnan(x))])\n",
    "    if len(a) == 0: return '       --        '\n",
    "    sd = a.std(ddof=1) if len(a) > 1 else 0.0\n",
    "    return f'{a.mean():>8.3f} ± {sd:6.3f}'\n",
    "\n",
    "for k, label in METRICS_SUMMARY:\n",
    "    fes_v = [f[k] for f, _ in paired]\n",
    "    nof_v = [n[k] for _, n in paired]\n",
    "    deltas = [f - n for f, n in zip(fes_v, nof_v)\n",
    "              if f is not None and n is not None\n",
    "              and not (isinstance(f, float) and np.isnan(f))\n",
    "              and not (isinstance(n, float) and np.isnan(n))]\n",
    "    if not deltas:\n",
    "        print(f'{label:<28} {\"(no data)\":<18} {\"\":<18}')\n",
    "        continue\n",
    "    mean_d, lo, hi = boot_paired_ci(deltas)\n",
    "    d_arr = np.array(deltas)\n",
    "    n_pos, n_neg = int((d_arr > 0).sum()), int((d_arr < 0).sum())\n",
    "    ci_crosses_zero = (lo < 0 < hi)\n",
    "    marker = '  ' if ci_crosses_zero else ' *'\n",
    "    print(f'{label:<28} {fmt_mean_sd(fes_v):>18} {fmt_mean_sd(nof_v):>18} '\n",
    "          f'{mean_d:>+9.3f} [{lo:>+7.3f},{hi:>+7.3f}]{marker}  '\n",
    "          f'{n_pos}/{n_pos+n_neg}')\n",
    "\n",
    "print('\\n* = 95% bootstrap CI excludes zero (suggestive at this n).')\n",
    "print('Sign column: number of (subject × pair) comparisons where FES > NOFES.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee6e054e",
   "metadata": {},
   "source": [
    "## Riemannian Generic Recentering & Discriminability\n",
    "\n",
    "To investigate whether discriminability changes resulting from FES feedback are hidden by pure accuracy levels, we replicate the robust transfer learning baseline (Riemannian Generic recentering, e.g., Zanini et al., 2018). \n",
    "\n",
    "Here we calculated a parameter-independent discriminability metric mathematically isolated from the fixed CSP projections. For each session, covariances are calculated and trace-normalized. We define the global reference point $C_{ref}$ as the Riemannian mean of the resting state trials and calculate discriminability as the native distance between class centers: $\\delta_R(C_{MI}, C_{REST})$ upon the Riemannian manifold of SPD matrices.\n",
    "\n",
    "By projecting each session's testing data to the identity $C_{ref}^{-1/2} X$, the identical offline decoder is evaluated on harmonized session distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4cb58a3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:157: SyntaxWarning: invalid escape sequence '\\d'\n",
      "<>:157: SyntaxWarning: invalid escape sequence '\\d'\n",
      "/var/folders/98/qwcfyxcd0c12f9zp2wjlf1x80000gn/T/ipykernel_41885/1640400890.py:157: SyntaxWarning: invalid escape sequence '\\d'\n",
      "  ax.set_ylabel('Riemannian distance (MI vs REST)\\n$\\delta_R(C_{MI}, C_{REST})$')\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 3.9375691313508494e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 3.669963803608868e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 3.645524816095128e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 3.663373071280961e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 3.6914791949032847e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.6459381005574144e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.574740959007683e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.6370933350269555e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.5834058892971734e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.619226858195733e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.802433522130159e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 3.223125920536493e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.7162919724570517e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.9009604106934567e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.750509135686626e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.913175562960757e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.7225637711028584e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 3.0329918184869934e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.8006281048873087e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 3.0723573088052246e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.7691939412319846e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 3.007728608121999e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 3.422868575242497e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 3.0645254120286126e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.8596526698824483e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.2336790648652972e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.8526260428090153e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.2948400779817427e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.850686253077868e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.826465184871438e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.8727151431041065e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.793714914157437e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.3041236094301544e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.7244569473274707e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.2527220293745332e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.714964358671654e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.275182870324487e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.455653261686123e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.308158695654615e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.796099171627302e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.3019580157253367e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.711078152814985e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.3069898990797965e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.646265488710886e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.4338975954189277e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n",
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/_lib/_util.py:1181: RuntimeWarning: logm result may be inaccurate, approximate err = 2.5362700984456297e-13\n",
      "  return f(*arrays, *other_args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Riemannian Alignment & Discriminability Metrics ===\n",
      "\n",
      "Metric                                      FES              NOFES  paired Δ            95% CI (boot)\n",
      "-----------------------------------------------------------------------------------------------------\n",
      "Riemannian MI–REST Distance      1.702 ±  0.588     1.687 ±  0.422    +0.016 [ -0.395, +0.371]  \n",
      "Aligned CSPLDA test acc          0.690 ±  0.147     0.705 ±  0.197    -0.015 [ -0.101, +0.070]  \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Saved: riemannian_discriminability.png\n"
     ]
    }
   ],
   "source": [
    "from scipy.linalg import sqrtm, logm, eigh, inv, norm, expm\n",
    "\n",
    "def riemannian_mean_cov(covs, tol=1e-5, max_iter=30):\n",
    "    \"\"\"Compute Riemannian mean of SPD matrices using iterative fixed-point algorithm.\"\"\"\n",
    "    covs = [np.asarray(C, dtype=np.float64) for C in covs]\n",
    "    # Add small regularization to ensure SPD\n",
    "    for i in range(len(covs)):\n",
    "        covs[i] = covs[i] + 1e-5 * np.trace(covs[i]) / covs[i].shape[0] * np.eye(covs[i].shape[0])\n",
    "    C_mean = np.mean(covs, axis=0) # Init with Euclidean mean\n",
    "    for _ in range(max_iter):\n",
    "        C_sq_inv = inv(sqrtm(C_mean))\n",
    "        tangent_sum = np.zeros_like(C_mean)\n",
    "        for C in covs:\n",
    "            tangent_sum += np.real(logm(C_sq_inv @ C @ C_sq_inv))\n",
    "        tangent_sum /= len(covs)\n",
    "        \n",
    "        C_mean = sqrtm(C_mean) @ expm(tangent_sum) @ sqrtm(C_mean)\n",
    "        C_mean = np.real(C_mean)\n",
    "        if norm(tangent_sum) < tol:\n",
    "            break\n",
    "    return C_mean\n",
    "\n",
    "def riemannian_dist(A, B):\n",
    "    \"\"\"Riemannian distance between symmetric positive-definite matrices A and B.\"\"\"\n",
    "    A_reg = A + 1e-6 * np.trace(A) / A.shape[0] * np.eye(A.shape[0])\n",
    "    B_reg = B + 1e-6 * np.trace(B) / B.shape[0] * np.eye(B.shape[0])\n",
    "    evals = np.clip(np.real(eigh(A_reg, B_reg, eigvals_only=True)), 1e-12, np.inf)\n",
    "    return np.sqrt(np.sum(np.log(evals)**2))\n",
    "\n",
    "def recenter_data(X, y):\n",
    "    \"\"\"\n",
    "    Riemannian Generic Recentering (Zanini et al. 2018).\n",
    "    Calculates reference covariance C_ref from resting state (y=0)\n",
    "    and transforms X -> R @ X where R = C_ref^{-1/2}\n",
    "    \"\"\"\n",
    "    # compute trace-normalized covariances\n",
    "    covs = np.einsum('ijk,ilk->ijl', X, X)\n",
    "    covs /= np.trace(covs, axis1=1, axis2=2)[:, None, None]\n",
    "    \n",
    "    covs_rest = covs[y == 0]\n",
    "    if len(covs_rest) == 0:\n",
    "        return X, None, np.nan\n",
    "        \n",
    "    C_ref = riemannian_mean_cov(covs_rest)\n",
    "    \n",
    "    # Alignment transformation R = C_ref^{-1/2}\n",
    "    R = np.real(inv(sqrtm(C_ref)))\n",
    "    X_aligned = np.einsum('ij,kjl->kil', R, X)\n",
    "    \n",
    "    # Riemannian discriminability (Distance between MI center and REST center)\n",
    "    covs_mi = covs[y == 1]\n",
    "    if len(covs_mi) > 0:\n",
    "        C_mi = riemannian_mean_cov(covs_mi)\n",
    "        dist = riemannian_dist(C_mi, C_ref)\n",
    "    else:\n",
    "        dist = np.nan\n",
    "        \n",
    "    return X_aligned, C_ref, dist\n",
    "\n",
    "MIN_TEST_TRIALS = 10\n",
    "ra_results = []\n",
    "\n",
    "for subj in subjects:\n",
    "    subj_ses = sessions[subj]\n",
    "    for pair in PAIRS:\n",
    "        needed = (pair['train'], pair['online_fes'], pair['online_nofes'])\n",
    "        if any(k not in subj_ses for k in needed): continue\n",
    "        \n",
    "        train = subj_ses[pair['train']]\n",
    "        if set(np.unique(train['y'])) != {0, 1}: continue\n",
    "        \n",
    "        # 1. Recenter Training data\n",
    "        X_train_r, _, _ = recenter_data(train['X'], train['y'])\n",
    "        \n",
    "        # 2. Fit CSPLDA on recentered offline train data\n",
    "        clf = CSPLDA(n_csp=N_CSP).fit(X_train_r, train['y'])\n",
    "        train_acc = (clf.predict(X_train_r) == train['y']).mean()\n",
    "        \n",
    "        for cond_key, cond_label in [('online_fes', 'FES'), ('online_nofes', 'NOFES')]:\n",
    "            te = subj_ses[pair[cond_key]]\n",
    "            if len(te['y']) < MIN_TEST_TRIALS or set(np.unique(te['y'])) != {0, 1}: continue\n",
    "            \n",
    "            # 3. Recenter Testing data (Online sets)\n",
    "            X_test_r, _, riemann_dist = recenter_data(te['X'], te['y'])\n",
    "            \n",
    "            # 4. Evaluate decoder on recentered test data\n",
    "            res = evaluate(clf, X_test_r, te['y'])\n",
    "            \n",
    "            y_true = res['y']\n",
    "            y_pred = res['pred']\n",
    "            acc = (y_true == y_pred).mean()\n",
    "            mi_acc = (y_pred[y_true == 1] == 1).mean() if (y_true == 1).any() else np.nan\n",
    "            rest_acc = (y_pred[y_true == 0] == 0).mean() if (y_true == 0).any() else np.nan\n",
    "            \n",
    "            ra_results.append({\n",
    "                'subject': subj,\n",
    "                'pair': pair['name'],\n",
    "                'condition': cond_label,\n",
    "                'train_acc': train_acc,\n",
    "                'test_acc': acc,\n",
    "                'mi_acc': mi_acc,\n",
    "                'rest_acc': rest_acc,\n",
    "                'riemann_dist': riemann_dist\n",
    "            })\n",
    "\n",
    "print(\"=== Riemannian Alignment & Discriminability Metrics ===\\n\")\n",
    "hdr = f'{\"Metric\":<28} {\"FES\":>18} {\"NOFES\":>18} {\"paired Δ\":>9} {\"95% CI (boot)\":>24}'\n",
    "print(hdr); print('-'*len(hdr))\n",
    "\n",
    "def fmt_ra(arr):\n",
    "    if len(arr) == 0: return ' -- '\n",
    "    return f'{np.mean(arr):>8.3f} ± {np.std(arr, ddof=1):6.3f}'\n",
    "\n",
    "metrics = [\n",
    "    ('riemann_dist', 'Riemannian MI–REST Distance'),\n",
    "    ('test_acc', 'Aligned CSPLDA test acc')\n",
    "]\n",
    "\n",
    "for m_key, m_label in metrics:\n",
    "    fes_vals, nof_vals, deltas = [], [], []\n",
    "    for subj in subjects:\n",
    "        for pair in PAIRS:\n",
    "            fes = next((r for r in ra_results if r['subject']==subj and r['pair']==pair['name'] and r['condition']=='FES'), None)\n",
    "            nof = next((r for r in ra_results if r['subject']==subj and r['pair']==pair['name'] and r['condition']=='NOFES'), None)\n",
    "            if fes and nof and not np.isnan(fes[m_key]) and not np.isnan(nof[m_key]):\n",
    "                fes_vals.append(fes[m_key])\n",
    "                nof_vals.append(nof[m_key])\n",
    "                deltas.append(fes[m_key] - nof[m_key])\n",
    "    \n",
    "    if len(deltas) > 0:\n",
    "        mean_d, lo, hi = boot_paired_ci(deltas)\n",
    "        marker = '  ' if lo < 0 < hi else ' *'\n",
    "        print(f'{m_label:<28} {fmt_ra(fes_vals):>18} {fmt_ra(nof_vals):>18} '\n",
    "              f'{mean_d:>+9.3f} [{lo:>+7.3f},{hi:>+7.3f}]{marker}')\n",
    "\n",
    "# Plot Riemannian Distance (Discriminability) distribution\n",
    "fes_dists = [r['riemann_dist'] for r in ra_results if r['condition'] == 'FES']\n",
    "nof_dists = [r['riemann_dist'] for r in ra_results if r['condition'] == 'NOFES']\n",
    "\n",
    "if fes_dists and nof_dists:\n",
    "    fig, ax = plt.subplots(figsize=(6, 5))\n",
    "    means = [np.mean(fes_dists), np.mean(nof_dists)]\n",
    "    sems = [np.std(fes_dists, ddof=1)/np.sqrt(len(fes_dists)),\n",
    "            np.std(nof_dists, ddof=1)/np.sqrt(len(nof_dists))]\n",
    "    ax.bar(['ONLINE FES', 'ONLINE NOFES'], means, yerr=sems, \n",
    "           color=[cond_color['FES'], cond_color['NOFES']], capsize=6, edgecolor='white')\n",
    "\n",
    "    # Paired lines\n",
    "    for subj in subjects:\n",
    "        for pair in PAIRS:\n",
    "            fes = next((r for r in ra_results if r['subject']==subj and r['pair']==pair['name'] and r['condition']=='FES'), None)\n",
    "            nof = next((r for r in ra_results if r['subject']==subj and r['pair']==pair['name'] and r['condition']=='NOFES'), None)\n",
    "            if fes and nof:\n",
    "                ax.plot([0, 1], [fes['riemann_dist'], nof['riemann_dist']], color='k', alpha=0.3, lw=1)\n",
    "                ax.scatter([0, 1], [fes['riemann_dist'], nof['riemann_dist']], color='k', alpha=0.5, s=20, zorder=3)\n",
    "\n",
    "    ax.set_ylabel('Riemannian distance (MI vs REST)\\n$\\delta_R(C_{MI}, C_{REST})$')\n",
    "    ax.set_title('Inherent Riemannian Discriminability\\n(Independent of classifier projection)', fontweight='bold')\n",
    "    ax.spines[['top', 'right']].set_visible(False)\n",
    "    ax.grid(axis='y', alpha=0.3)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('riemannian_discriminability.png', dpi=150, bbox_inches='tight')\n",
    "    plt.show()\n",
    "    print('\\nSaved: riemannian_discriminability.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09f20fd7",
   "metadata": {},
   "source": [
    "## Nonstationarity: Drift from Offline Calibration to Online Session\n",
    "\n",
    "This analysis measures the covariate shift (drift) by calculating the Riemannian distance between the class manifold in the offline calibration session and the class manifold in the subsequent online session. \n",
    "- **FES Drift:** Distance from OFFLINE_FES to ONLINE_FES\n",
    "- **NOFES Drift:** Distance from OFFLINE_NOFES to ONLINE_NOFES\n",
    "\n",
    "This is done separately for the MI state and the REST state. High distances indicate that the brain state during the online task has drifted significantly from what the classifier learned during the offline phase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "17f73b20",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:112: SyntaxWarning: invalid escape sequence '\\d'\n",
      "<>:112: SyntaxWarning: invalid escape sequence '\\d'\n",
      "/var/folders/98/qwcfyxcd0c12f9zp2wjlf1x80000gn/T/ipykernel_41885/2712111310.py:112: SyntaxWarning: invalid escape sequence '\\d'\n",
      "  axes[0].set_ylabel('Riemannian distance (Offline vs Online)\\n$\\delta_R(C_{train}, C_{test})$')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Offline to Online Nonstationarity (Riemannian Drift) ===\n",
      "\n",
      "Metric                                      FES              NOFES  paired Δ            95% CI (boot)\n",
      "-----------------------------------------------------------------------------------------------------\n",
      "MI Drift (Offline->Online)       2.470 ±  1.075     2.248 ±  0.935    +0.222 [ -0.245, +0.869]  \n",
      "REST Drift (Offline->Online)     2.534 ±  1.087     2.354 ±  0.987    +0.180 [ -0.375, +0.836]  \n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Saved: riemannian_drift_offline_to_online.png\n"
     ]
    }
   ],
   "source": [
    "drift_results = []\n",
    "\n",
    "for subj in subjects:\n",
    "    subj_ses = sessions[subj]\n",
    "    \n",
    "    # We want to measure the drift from offline to the corresponding online session:\n",
    "    # 1. FES Drift: OFFLINE_FES -> ONLINE_FES\n",
    "    # 2. NOFES Drift: OFFLINE_NOFES -> ONLINE_NOFES\n",
    "    drift_pairs = [\n",
    "        ('FES', PAIRS[0]['train'], PAIRS[0]['online_fes']),       \n",
    "        ('NOFES', PAIRS[1]['train'], PAIRS[1]['online_nofes'])    \n",
    "    ]\n",
    "    \n",
    "    for cond_label, train_key, test_key in drift_pairs:\n",
    "        if train_key not in subj_ses or test_key not in subj_ses:\n",
    "            continue\n",
    "            \n",
    "        train = subj_ses[train_key]\n",
    "        test = subj_ses[test_key]\n",
    "        \n",
    "        # Internal helper to calculate regularized trace-normalized covariances\n",
    "        def get_covs(X):\n",
    "            c = np.einsum('ijk,ilk->ijl', X, X)\n",
    "            c /= np.trace(c, axis1=1, axis2=2)[:, None, None]\n",
    "            # Add small regularization to ensure they are SPD for the Riemannian distance\n",
    "            for i in range(len(c)):\n",
    "                c[i] = c[i] + 1e-5 * np.trace(c[i]) / c[i].shape[0] * np.eye(c[i].shape[0])\n",
    "            return c\n",
    "\n",
    "        train_covs = get_covs(train['X'])\n",
    "        test_covs = get_covs(test['X'])\n",
    "        \n",
    "        # Pull out the MI and REST manifolds\n",
    "        train_mi, train_rest = train_covs[train['y'] == 1], train_covs[train['y'] == 0]\n",
    "        test_mi, test_rest = test_covs[test['y'] == 1], test_covs[test['y'] == 0]\n",
    "        \n",
    "        if len(train_mi) == 0 or len(train_rest) == 0 or len(test_mi) == 0 or len(test_rest) == 0:\n",
    "            continue\n",
    "            \n",
    "        # Riemannian mean for each phase/class\n",
    "        C_train_mi = riemannian_mean_cov(train_mi)\n",
    "        C_train_rest = riemannian_mean_cov(train_rest)\n",
    "        \n",
    "        C_test_mi = riemannian_mean_cov(test_mi)\n",
    "        C_test_rest = riemannian_mean_cov(test_rest)\n",
    "        \n",
    "        # Calculate Riemannian Drift (distance) between offline and online centers\n",
    "        mi_drift = riemannian_dist(C_train_mi, C_test_mi)\n",
    "        rest_drift = riemannian_dist(C_train_rest, C_test_rest)\n",
    "        \n",
    "        drift_results.append({\n",
    "            'subject': subj,\n",
    "            'condition': cond_label,\n",
    "            'mi_drift': mi_drift,\n",
    "            'rest_drift': rest_drift\n",
    "        })\n",
    "\n",
    "print(\"=== Offline to Online Nonstationarity (Riemannian Drift) ===\\n\")\n",
    "hdr = f'{\"Metric\":<28} {\"FES\":>18} {\"NOFES\":>18} {\"paired Δ\":>9} {\"95% CI (boot)\":>24}'\n",
    "print(hdr); print('-'*len(hdr))\n",
    "\n",
    "drift_metrics = [\n",
    "    ('mi_drift', 'MI Drift (Offline->Online)'),\n",
    "    ('rest_drift', 'REST Drift (Offline->Online)')\n",
    "]\n",
    "\n",
    "for m_key, m_label in drift_metrics:\n",
    "    fes_vals, nof_vals, deltas = [], [], []\n",
    "    for subj in subjects:\n",
    "        fes = next((r for r in drift_results if r['subject']==subj and r['condition']=='FES'), None)\n",
    "        nof = next((r for r in drift_results if r['subject']==subj and r['condition']=='NOFES'), None)\n",
    "        if fes and nof and not np.isnan(fes[m_key]) and not np.isnan(nof[m_key]):\n",
    "            fes_vals.append(fes[m_key])\n",
    "            nof_vals.append(nof[m_key])\n",
    "            deltas.append(fes[m_key] - nof[m_key])\n",
    "    \n",
    "    if len(deltas) > 0:\n",
    "        mean_d, lo, hi = boot_paired_ci(deltas)\n",
    "        marker = '  ' if lo < 0 < hi else ' *'\n",
    "        print(f'{m_label:<28} {fmt_ra(fes_vals):>18} {fmt_ra(nof_vals):>18} '\n",
    "              f'{mean_d:>+9.3f} [{lo:>+7.3f},{hi:>+7.3f}]{marker}')\n",
    "\n",
    "\n",
    "# ── Figure ──\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10, 5), sharey=True)\n",
    "fig.suptitle('Manifold Drift: Offline Calibration to Online Session\\nMeasured by Riemannian Distance', fontweight='bold')\n",
    "\n",
    "for ax, (m_key, m_label) in zip(axes, drift_metrics):\n",
    "    fes_dists = [r[m_key] for r in drift_results if r['condition'] == 'FES']\n",
    "    nof_dists = [r[m_key] for r in drift_results if r['condition'] == 'NOFES']\n",
    "\n",
    "    if fes_dists and nof_dists:\n",
    "        means = [np.mean(fes_dists), np.mean(nof_dists)]\n",
    "        sems = [np.std(fes_dists, ddof=1)/np.sqrt(len(fes_dists)),\n",
    "                np.std(nof_dists, ddof=1)/np.sqrt(len(nof_dists))]\n",
    "        \n",
    "        ax.bar(['FES Path\\n(FES Calib -> Online)', 'NOFES Path\\n(NOFES Calib -> Online)'], means, yerr=sems, \n",
    "               color=[cond_color['FES'], cond_color['NOFES']], capsize=6, edgecolor='white')\n",
    "\n",
    "        # Paired lines\n",
    "        for subj in subjects:\n",
    "            fes = next((r for r in drift_results if r['subject']==subj and r['condition']=='FES'), None)\n",
    "            nof = next((r for r in drift_results if r['subject']==subj and r['condition']=='NOFES'), None)\n",
    "            if fes and nof:\n",
    "                ax.plot([0, 1], [fes[m_key], nof[m_key]], color='k', alpha=0.3, lw=1)\n",
    "                ax.scatter([0, 1], [fes[m_key], nof[m_key]], color='k', alpha=0.5, s=20, zorder=3)\n",
    "\n",
    "    ax.set_title(m_label, fontweight='bold', fontsize=11)\n",
    "    ax.spines[['top', 'right']].set_visible(False)\n",
    "    ax.grid(axis='y', alpha=0.3)\n",
    "\n",
    "axes[0].set_ylabel('Riemannian distance (Offline vs Online)\\n$\\delta_R(C_{train}, C_{test})$')\n",
    "plt.tight_layout()\n",
    "plt.savefig('riemannian_drift_offline_to_online.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()\n",
    "print('\\nSaved: riemannian_drift_offline_to_online.png')"
   ]
  }
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