{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d8960e56",
   "metadata": {},
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "578c9128",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T03:09:19.274466Z",
     "iopub.status.busy": "2026-04-22T03:09:19.273226Z",
     "iopub.status.idle": "2026-04-22T03:09:19.280048Z",
     "shell.execute_reply": "2026-04-22T03:09:19.279206Z"
    }
   },
   "outputs": [],
   "source": [
    "# Install dependencies if needed\n",
    "# !pip install pyxdf mne scipy numpy matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "857b22c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T03:09:19.283002Z",
     "iopub.status.busy": "2026-04-22T03:09:19.282756Z",
     "iopub.status.idle": "2026-04-22T03:09:20.342432Z",
     "shell.execute_reply": "2026-04-22T03:09:20.341961Z"
    }
   },
   "outputs": [],
   "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",
    "\n",
    "plt.rcParams.update({'font.size': 11, 'figure.dpi': 120})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe68bf0e",
   "metadata": {},
   "source": [
    "## Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dc4b2c55",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T03:09:20.343870Z",
     "iopub.status.busy": "2026-04-22T03:09:20.343760Z",
     "iopub.status.idle": "2026-04-22T03:09:20.346854Z",
     "shell.execute_reply": "2026-04-22T03:09:20.346506Z"
    }
   },
   "outputs": [],
   "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",
    "# Sessions are stored with compound key \"{ses_id}_{kind}_{stim}\" and looked up\n",
    "# positionally: idx=0 is the first session of that (kind, stim) type sorted by\n",
    "# key, idx=1 is the second. This handles subjects like 002 where OFFLINE and\n",
    "# ONLINE sessions share the same ses-Sxxx number in the filename.\n",
    "PAIRS = [\n",
    "    {'name':  'Pair1 (train=OFFLINE_FES)',\n",
    "     'train':        {'kind': 'OFFLINE', 'stim': 'FES',   'idx': 0},\n",
    "     'online_fes':   {'kind': 'ONLINE',  'stim': 'FES',   'idx': 0},\n",
    "     'online_nofes': {'kind': 'ONLINE',  'stim': 'NOFES', 'idx': 0},\n",
    "    },\n",
    "    {'name':  'Pair2 (train=OFFLINE_NOFES)',\n",
    "     'train':        {'kind': 'OFFLINE', 'stim': 'NOFES', 'idx': 0},\n",
    "     'online_fes':   {'kind': 'ONLINE',  'stim': 'FES',   'idx': 1},\n",
    "     'online_nofes': {'kind': 'ONLINE',  'stim': 'NOFES', 'idx': 1},\n",
    "    },\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21a40df3",
   "metadata": {},
   "source": [
    "## Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e798b039",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T03:09:20.348424Z",
     "iopub.status.busy": "2026-04-22T03:09:20.348357Z",
     "iopub.status.idle": "2026-04-22T03:09:20.361153Z",
     "shell.execute_reply": "2026-04-22T03:09:20.360830Z"
    }
   },
   "outputs": [],
   "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",
    "def marker_accuracy(marker_data):\n",
    "    \"\"\"Online accuracy from EARLYSTOP markers: (MI_EARLYSTOP + REST_EARLYSTOP) / total trials.\n",
    "    Returns None for offline sessions (no EARLYSTOP markers present).\n",
    "    \"\"\"\n",
    "    n_mi_trials   = int((marker_data == MI_BEGIN).sum())\n",
    "    n_rest_trials = int((marker_data == REST_BEGIN).sum())\n",
    "    n_total = n_mi_trials + n_rest_trials\n",
    "    if n_total == 0:\n",
    "        return None\n",
    "    n_mi_correct   = int((marker_data == MI_EARLYSTOP).sum())\n",
    "    n_rest_correct = int((marker_data == REST_EARLYSTOP).sum())\n",
    "    # If no EARLYSTOP markers present at all, this is an offline session\n",
    "    if n_mi_correct + n_rest_correct == 0:\n",
    "        return None\n",
    "    return (n_mi_correct + n_rest_correct) / n_total\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, mk_acc\n",
    "    where mk_acc = marker-based online accuracy (None for offline sessions).\n",
    "    \"\"\"\n",
    "    eeg, eeg_ts, mk, mk_ts, ch_names, sfreq = load_xdf_file(filepath)\n",
    "\n",
    "    # Marker-based accuracy before any EEG processing (counts all trials as seen by the live system)\n",
    "    mk_acc = marker_accuracy(mk)\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, mk_acc\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": 5,
   "id": "d266216b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T03:09:20.362577Z",
     "iopub.status.busy": "2026-04-22T03:09:20.362504Z",
     "iopub.status.idle": "2026-04-22T03:09:59.345624Z",
     "shell.execute_reply": "2026-04-22T03:09:59.271366Z"
    }
   },
   "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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  002/S001  OFFLINE FES    n= 85  (MI=43, REST=42)  rej=5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  002/S002  ONLINE  FES    n= 53  (MI=27, REST=26)  rej=7  mk_acc=0.883\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  002/S003  ONLINE  NOFES  n= 52  (MI=26, REST=26)  rej=8  mk_acc=0.833\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  002/S004  OFFLINE NOFES  n= 90  (MI=45, REST=45)  rej=0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  002/S005  ONLINE  NOFES  n= 60  (MI=30, REST=30)  rej=0  mk_acc=0.850\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  002/S006  ONLINE  FES    n= 56  (MI=27, REST=29)  rej=4  mk_acc=0.917\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  003/S001  OFFLINE FES    n= 89  (MI=44, REST=45)  rej=1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  003/S002  ONLINE  FES    n= 59  (MI=29, REST=30)  rej=1  mk_acc=0.750\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  003/S003  ONLINE  NOFES  n= 38  (MI=17, REST=21)  rej=0  mk_acc=0.763\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  003/S004  OFFLINE NOFES  n= 86  (MI=42, REST=44)  rej=4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  003/S005  ONLINE  NOFES  n= 43  (MI=19, REST=24)  rej=17  mk_acc=0.717\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  003/S006  ONLINE  FES    n= 52  (MI=23, REST=29)  rej=8  mk_acc=0.767\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  005/S001  OFFLINE FES    n= 90  (MI=45, REST=45)  rej=0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  005/S002  ONLINE  FES    n= 60  (MI=30, REST=30)  rej=0  mk_acc=0.800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  005/S003  ONLINE  NOFES  n= 59  (MI=29, REST=30)  rej=1  mk_acc=0.933\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  005/S004  OFFLINE NOFES  n= 89  (MI=44, REST=45)  rej=1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  005/S005  ONLINE  NOFES  n= 58  (MI=28, REST=30)  rej=2  mk_acc=0.783\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  005/S006  ONLINE  FES    n= 59  (MI=30, REST=29)  rej=1  mk_acc=0.917\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  009/S001  OFFLINE FES    n= 57  (MI=33, REST=24)  rej=33\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  009/S002  ONLINE  FES    n= 42  (MI=21, REST=21)  rej=18  mk_acc=0.717\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  009/S003  ONLINE  NOFES  n=  1  (MI=1, REST=0)  rej=59  mk_acc=0.717\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  009/S004  OFFLINE NOFES  n= 86  (MI=42, REST=44)  rej=4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  009/S005  ONLINE  NOFES  n= 60  (MI=30, REST=30)  rej=0  mk_acc=0.850\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  009/S006  ONLINE  FES    n= 50  (MI=26, REST=24)  rej=10  mk_acc=0.817\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, mk_acc = load_session_epochs(fp)\n",
    "    except Exception as e:\n",
    "        print(f'  ERROR {os.path.basename(fp)}: {e}')\n",
    "        continue\n",
    "\n",
    "    # Compound key prevents collision when multiple session types share the same ses_id\n",
    "    key = f'{ses_id}_{kind}_{stim}'\n",
    "    sessions.setdefault(subj, {})[key] = dict(\n",
    "        X=X, y=y, kind=kind, stim=stim,\n",
    "        ch_names=ch_names, sfreq=sfreq,\n",
    "        mk_acc=mk_acc, file=os.path.basename(fp))\n",
    "    total_rej += n_rej\n",
    "\n",
    "    acc_str = f'  mk_acc={mk_acc:.3f}' if mk_acc is not None else ''\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}{acc_str}')\n",
    "\n",
    "subjects = sorted(sessions.keys())\n",
    "print(f'\\nLoaded {len(subjects)} subject(s): {subjects}  |  '\n",
    "      f'total artifact-rejected epochs: {total_rej}')\n",
    "\n",
    "\n",
    "def pick_session(subj_ses, kind, stim, idx):\n",
    "    \"\"\"Return the idx-th session matching (kind, stim), sorted by key. None if not enough.\"\"\"\n",
    "    matches = sorted(\n",
    "        (v for v in subj_ses.values() if v['kind'] == kind and v['stim'] == stim),\n",
    "        key=lambda v: v['file']   # alphabetical file order = recording order\n",
    "    )\n",
    "    return matches[idx] if idx < len(matches) else None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b8c8bea",
   "metadata": {},
   "source": [
    "## Verify Session Layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "611baf23",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T03:09:59.485105Z",
     "iopub.status.busy": "2026-04-22T03:09:59.481587Z",
     "iopub.status.idle": "2026-04-22T03:09:59.616890Z",
     "shell.execute_reply": "2026-04-22T03:09:59.612929Z"
    }
   },
   "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": 7,
   "id": "f5e80da3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T03:09:59.637480Z",
     "iopub.status.busy": "2026-04-22T03:09:59.637343Z",
     "iopub.status.idle": "2026-04-22T03:10:03.257068Z",
     "shell.execute_reply": "2026-04-22T03:10:03.255453Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[009] Pair1 (train=OFFLINE_FES) / NOFES: only 1 clean epochs — acc=0.717 (marker-based), skipping EEG metrics\n",
      "\n",
      "Subj  Pair                         Cond      n  trainAcc   mkAcc   |marg|   Fisher    muSNR\n",
      "-------------------------------------------------------------------------------------------\n",
      "002   Pair1 (train=OFFLINE_FES)    FES      53     0.659   0.883    0.778    0.288    1.197\n",
      "002   Pair1 (train=OFFLINE_FES)    NOFES    52     0.659   0.833    1.162    0.073    1.452\n",
      "002   Pair2 (train=OFFLINE_NOFES)  FES      56     0.811   0.917    1.012    3.133    1.665\n",
      "002   Pair2 (train=OFFLINE_NOFES)  NOFES    60     0.811   0.850    0.756    0.695    1.576\n",
      "003   Pair1 (train=OFFLINE_FES)    FES      59     0.843   0.750    0.879    0.017    1.347\n",
      "003   Pair1 (train=OFFLINE_FES)    NOFES    38     0.843   0.763    0.910    0.683    1.219\n",
      "003   Pair2 (train=OFFLINE_NOFES)  FES      52     0.907   0.767    1.166    0.000    1.273\n",
      "003   Pair2 (train=OFFLINE_NOFES)  NOFES    43     0.907   0.717    1.071    0.029    1.296\n",
      "005   Pair1 (train=OFFLINE_FES)    FES      60     0.978   0.800    3.346    2.498    2.695\n",
      "005   Pair1 (train=OFFLINE_FES)    NOFES    59     0.978   0.933    3.620    3.029    2.399\n",
      "005   Pair2 (train=OFFLINE_NOFES)  FES      59     1.000   0.917    5.740    6.291    2.911\n",
      "005   Pair2 (train=OFFLINE_NOFES)  NOFES    58     1.000   0.783    5.261    4.325    2.287\n",
      "009   Pair1 (train=OFFLINE_FES)    FES      42     0.667   0.717    0.304    0.040    1.326\n",
      "009   Pair2 (train=OFFLINE_NOFES)  FES      50     1.000   0.817    4.673    6.192    1.594\n",
      "009   Pair2 (train=OFFLINE_NOFES)  NOFES    60     1.000   0.850    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",
    "        train = pick_session(subj_ses, pair['train']['kind'],  pair['train']['stim'],  pair['train']['idx'])\n",
    "        te_f  = pick_session(subj_ses, pair['online_fes']['kind'],   pair['online_fes']['stim'],   pair['online_fes']['idx'])\n",
    "        te_n  = pick_session(subj_ses, pair['online_nofes']['kind'], pair['online_nofes']['stim'], pair['online_nofes']['idx'])\n",
    "\n",
    "        missing = [role for role, ses in [('train', train), ('online_fes', te_f), ('online_nofes', te_n)] if ses is None]\n",
    "        if missing:\n",
    "            print(f'[{subj}] {pair[\"name\"]}: no session found for {missing} — skipping')\n",
    "            continue\n",
    "\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 te, cond_label in [(te_f, 'FES'), (te_n, 'NOFES')]:\n",
    "            acc = te['mk_acc']\n",
    "            if acc is None:\n",
    "                print(f'[{subj}] {pair[\"name\"]} / {cond_label}: no EARLYSTOP markers — skipping')\n",
    "                continue\n",
    "\n",
    "            if len(te['y']) < MIN_TEST_TRIALS or set(np.unique(te['y'])) != {0, 1}:\n",
    "                print(f'[{subj}] {pair[\"name\"]} / {cond_label}: only {len(te[\"y\"])} clean epochs — '\n",
    "                      f'acc={acc:.3f} (marker-based), skipping EEG metrics')\n",
    "                continue\n",
    "\n",
    "            res   = evaluate(clf, te['X'], te['y'])\n",
    "            snr_s = spectral_snr(te['X'], te['y'], motor_idx_global, te['sfreq'])\n",
    "\n",
    "            results.append(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=acc,\n",
    "                amp=res['amp'], fisher=res['fisher'], mu_snr=snr_s,\n",
    "                margin=res['margin'], y_test=res['y'], pred=res['pred'],\n",
    "            ))\n",
    "\n",
    "hdr = (f'{\"Subj\":<5} {\"Pair\":<28} {\"Cond\":<6} {\"n\":>4} '\n",
    "       f'{\"trainAcc\":>9} {\"mkAcc\":>7} {\"|marg|\":>8} {\"Fisher\":>8} {\"muSNR\":>8}')\n",
    "print('\\n' + hdr)\n",
    "print('-' * len(hdr))\n",
    "for r in results:\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} {r[\"amp\"]:>8.3f} '\n",
    "          f'{r[\"fisher\"]:>8.3f} {r[\"mu_snr\"]:>8.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ab81600",
   "metadata": {},
   "source": [
    "---\n",
    "## Figure 1 — Per-metric comparison (FES vs NOFES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d53e63b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T03:10:03.262351Z",
     "iopub.status.busy": "2026-04-22T03:10:03.262228Z",
     "iopub.status.idle": "2026-04-22T03:10:04.250444Z",
     "shell.execute_reply": "2026-04-22T03:10:04.249983Z"
    }
   },
   "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": 9,
   "id": "393042a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T03:10:04.254162Z",
     "iopub.status.busy": "2026-04-22T03:10:04.254059Z",
     "iopub.status.idle": "2026-04-22T03:10:05.210590Z",
     "shell.execute_reply": "2026-04-22T03:10:05.210168Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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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": 10,
   "id": "75df404b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T03:10:05.215990Z",
     "iopub.status.busy": "2026-04-22T03:10:05.215881Z",
     "iopub.status.idle": "2026-04-22T03:10:05.485686Z",
     "shell.execute_reply": "2026-04-22T03:10:05.485120Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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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": "b3db60ba",
   "metadata": {},
   "source": [
    "---\n",
    "## Summary Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cf55268e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T03:10:05.496520Z",
     "iopub.status.busy": "2026-04-22T03:10:05.496412Z",
     "iopub.status.idle": "2026-04-22T03:10:05.506439Z",
     "shell.execute_reply": "2026-04-22T03:10:05.506014Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Aggregate across 7 complete (subject × pair) comparisons ===\n",
      "\n",
      "Metric                              FES (mean ± sd)      NOFES (mean ± sd)     paired Δ\n",
      "---------------------------------------------------------------------------------------\n",
      "Classification accuracy           0.836 ±  0.070       0.819 ±  0.071        +0.017\n",
      "Classification amplitude          2.513 ±  2.062       2.362 ±  1.813        +0.151\n",
      "Fisher ratio (test SNR)           2.631 ±  2.757       2.542 ±  3.263        +0.090\n",
      "μ-band SNR  (REST/MI)             1.812 ±  0.700       1.747 ±  0.480        +0.065\n",
      "\n",
      "  acc         FES > NOFES in 4/7 comparisons  (NOFES > FES in 3)\n",
      "  |margin|    FES > NOFES in 4/7 comparisons  (NOFES > FES in 3)\n",
      "  Fisher      FES > NOFES in 3/7 comparisons  (NOFES > FES in 4)\n",
      "  μ-SNR       FES > NOFES in 4/7 comparisons  (NOFES > FES in 3)\n"
     ]
    }
   ],
   "source": [
    "# Build only pairs where BOTH FES and NOFES survived evaluation\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:\n",
    "            paired.append((fes, nof))\n",
    "\n",
    "print(f'=== Aggregate across {len(paired)} complete (subject × pair) comparisons ===\\n')\n",
    "\n",
    "hdr = f'{\"Metric\":<28} {\"FES (mean ± sd)\":>22} {\"NOFES (mean ± sd)\":>22} {\"paired Δ\":>12}'\n",
    "print(hdr); print('-' * len(hdr))\n",
    "for k, label in [('acc',    'Classification accuracy'),\n",
    "                 ('amp',    'Classification amplitude'),\n",
    "                 ('fisher', 'Fisher ratio (test SNR)'),\n",
    "                 ('mu_snr', 'μ-band SNR  (REST/MI)')]:\n",
    "    fes_v = np.array([f[k] for f,_ in paired])\n",
    "    nof_v = np.array([n[k] for _,n in paired])\n",
    "    delta = fes_v - nof_v\n",
    "    sd = lambda a: a.std(ddof=1) if len(a) > 1 else 0.0\n",
    "    print(f'{label:<28} {fes_v.mean():>10.3f} ± {sd(fes_v):>6.3f}  '\n",
    "          f'{nof_v.mean():>10.3f} ± {sd(nof_v):>6.3f}  {delta.mean():>+12.3f}')\n",
    "\n",
    "# Sign test (simple)\n",
    "print()\n",
    "for k, label in [('acc','acc'), ('amp','|margin|'), ('fisher','Fisher'), ('mu_snr','μ-SNR')]:\n",
    "    d = np.array([f[k] - n[k] for f,n in paired])\n",
    "    n_pos = int((d > 0).sum()); n_neg = int((d < 0).sum())\n",
    "    print(f'  {label:<10}  FES > NOFES in {n_pos}/{len(d)} comparisons  (NOFES > FES in {n_neg})')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.7"
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 },
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