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ECE374N/Final Project/overall_analysis.ipynb

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{
"cells": [
{
"cell_type": "markdown",
subj2
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"id": "d8960e56",
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"metadata": {},
subj2
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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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},
{
"cell_type": "code",
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"execution_count": 1,
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"id": "578c9128",
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"execution": {
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"outputs": [],
"source": [
"# Install dependencies if needed\n",
"# !pip install pyxdf mne scipy numpy matplotlib"
]
},
{
"cell_type": "code",
subj2
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"execution_count": 2,
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"id": "857b22c0",
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}
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"outputs": [],
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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",
"\n",
"plt.rcParams.update({'font.size': 11, 'figure.dpi': 120})"
]
cd
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},
{
"cell_type": "markdown",
"id": "fe68bf0e",
"metadata": {},
"source": [
"## Configuration"
]
},
{
"cell_type": "code",
subj2
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"execution_count": 3,
cd
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"id": "dc4b2c55",
subj2
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"metadata": {
"execution": {
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"outputs": [],
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"source": [
"DATA_DIR = os.path.join(os.path.dirname(os.path.abspath('__file__')), 'Group 2 - Glove')\n",
"\n",
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"# 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",
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"\n",
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"TARGET_MARKERS = [100, 120, 140, 200, 220, 240]\n",
"\n",
"T_PRE = -1.0\n",
"T_POST = 5.0\n",
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"\n",
"# ── Preprocessing ────────────────────────────────────────────────────────────\n",
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"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",
subj2
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"\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",
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"# 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",
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"PAIRS = [\n",
" {'name': 'Pair1 (train=OFFLINE_FES)',\n",
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" '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",
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" {'name': 'Pair2 (train=OFFLINE_NOFES)',\n",
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" '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",
subj2
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"]"
]
cd
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},
{
"cell_type": "markdown",
"id": "21a40df3",
"metadata": {},
"source": [
"## Helper Functions"
]
},
{
"cell_type": "code",
subj2
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"execution_count": 4,
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"id": "e798b039",
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"execution": {
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}
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"outputs": [],
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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",
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" base = os.path.basename(path)\n",
" m_subj = _SUBJ_RE.search(base)\n",
" m_ses = _SESSION_RE.search(base)\n",
subj2
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" 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",
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" ptp = X.max(axis=-1) - X.min(axis=-1)\n",
subj2
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" 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",
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"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",
subj2
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"def load_session_epochs(filepath):\n",
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" \"\"\"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",
subj2
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" \"\"\"\n",
" eeg, eeg_ts, mk, mk_ts, ch_names, sfreq = load_xdf_file(filepath)\n",
"\n",
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" # Marker-based accuracy before any EEG processing (counts all trials as seen by the live system)\n",
" mk_acc = marker_accuracy(mk)\n",
"\n",
subj2
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" 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",
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" return X, y, ch_names, sfreq, n_rejected, mk_acc\n",
subj2
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"\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",
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" \"\"\"CSP log-var features + LDA. Ramoser 2000; Blankertz 2008.\n",
" Ledoit-Wolf shrinkage keeps the generalized eigenproblem well-posed after CAR.\n",
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" \"\"\"\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",
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" assert set(np.unique(y)) == {0, 1}\n",
subj2
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" 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",
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" return dict(amp=amp, fisher=fisher, margin=margin, y=y, pred=pred)\n",
subj2
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"\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)"
]
cd
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},
{
"cell_type": "markdown",
"id": "98d225db",
"metadata": {},
"source": [
"## Load Data"
]
},
{
"cell_type": "code",
subj2
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"execution_count": 5,
cd
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"id": "d266216b",
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"metadata": {
"execution": {
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"shell.execute_reply": "2026-04-22T03:09:59.271366Z"
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}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Found 24 XDF file(s).\n",
subj2
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"Preprocessing: notch 60 Hz → CAR → bandpass 8–30 Hz → baseline-correct → PTP-reject @ 100 µV\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
updated naming
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" 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"
subj2
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
updated naming
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" 003/S002 ONLINE FES n= 59 (MI=29, REST=30) rej=1 mk_acc=0.750\n"
subj2
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
updated naming
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" 003/S003 ONLINE NOFES n= 38 (MI=17, REST=21) rej=0 mk_acc=0.763\n"
subj2
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
updated naming
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" 003/S004 OFFLINE NOFES n= 86 (MI=42, REST=44) rej=4\n"
subj2
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
updated naming
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" 003/S005 ONLINE NOFES n= 43 (MI=19, REST=24) rej=17 mk_acc=0.717\n"
subj2
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
updated naming
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" 003/S006 ONLINE FES n= 52 (MI=23, REST=29) rej=8 mk_acc=0.767\n"
subj2
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
updated naming
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" 005/S001 OFFLINE FES n= 90 (MI=45, REST=45) rej=0\n"
subj2
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
updated naming
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" 005/S002 ONLINE FES n= 60 (MI=30, REST=30) rej=0 mk_acc=0.800\n"
subj2
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
updated naming
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" 005/S003 ONLINE NOFES n= 59 (MI=29, REST=30) rej=1 mk_acc=0.933\n"
subj2
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
updated naming
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" 005/S004 OFFLINE NOFES n= 89 (MI=44, REST=45) rej=1\n"
subj2
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
updated naming
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" 005/S005 ONLINE NOFES n= 58 (MI=28, REST=30) rej=2 mk_acc=0.783\n"
subj2
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
updated naming
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" 005/S006 ONLINE FES n= 59 (MI=30, REST=29) rej=1 mk_acc=0.917\n"
subj2
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
updated naming
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" 009/S001 OFFLINE FES n= 57 (MI=33, REST=24) rej=33\n"
subj2
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
updated naming
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" 009/S002 ONLINE FES n= 42 (MI=21, REST=21) rej=18 mk_acc=0.717\n"
subj2
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
updated naming
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" 009/S003 ONLINE NOFES n= 1 (MI=1, REST=0) rej=59 mk_acc=0.717\n"
subj2
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
updated naming
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" 009/S004 OFFLINE NOFES n= 86 (MI=42, REST=44) rej=4\n"
subj2
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
updated naming
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" 009/S005 ONLINE NOFES n= 60 (MI=30, REST=30) rej=0 mk_acc=0.850\n"
subj2
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
updated naming
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" 009/S006 ONLINE FES n= 50 (MI=26, REST=24) rej=10 mk_acc=0.817\n",
subj2
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"\n",
updated naming
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"Loaded 4 subject(s): ['002', '003', '005', '009'] | total artifact-rejected epochs: 184\n"
subj2
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]
}
],
"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",
updated naming
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"sessions = {}\n",
subj2
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"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",
updated naming
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" X, y, ch_names, sfreq, n_rej, mk_acc = load_session_epochs(fp)\n",
subj2
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" except Exception as e:\n",
" print(f' ERROR {os.path.basename(fp)}: {e}')\n",
" continue\n",
"\n",
updated naming
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" # 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",
subj2
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" X=X, y=y, kind=kind, stim=stim,\n",
updated naming
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" ch_names=ch_names, sfreq=sfreq,\n",
" mk_acc=mk_acc, file=os.path.basename(fp))\n",
subj2
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" total_rej += n_rej\n",
"\n",
updated naming
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" acc_str = f' mk_acc={mk_acc:.3f}' if mk_acc is not None else ''\n",
subj2
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" 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",
updated naming
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" f'rej={n_rej}{acc_str}')\n",
subj2
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"\n",
"subjects = sorted(sessions.keys())\n",
"print(f'\\nLoaded {len(subjects)} subject(s): {subjects} | '\n",
updated naming
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" 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"
subj2
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]
cd
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},
{
"cell_type": "markdown",
"id": "7b8c8bea",
"metadata": {},
subj2
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"source": [
"## Verify Session Layout"
]
cd
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},
{
"cell_type": "code",
subj2
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"execution_count": 6,
cd
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"id": "611baf23",
subj2
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"metadata": {
"execution": {
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"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"
subj2
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}
},
"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}')"
]
cd
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},
{
"cell_type": "markdown",
"id": "70922abb",
"metadata": {},
subj2
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"source": [
"## Train CSP + LDA on OFFLINE, Evaluate on ONLINE"
]
cd
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},
{
"cell_type": "code",
subj2
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"execution_count": 7,
cd
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"id": "f5e80da3",
subj2
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"metadata": {
"execution": {
updated naming
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"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"
subj2
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}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
updated naming
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"[009] Pair1 (train=OFFLINE_FES) / NOFES: only 1 clean epochs — acc=0.717 (marker-based), skipping EEG metrics\n",
subj2
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"\n",
updated naming
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"Subj Pair Cond n trainAcc mkAcc |marg| Fisher muSNR\n",
subj2
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"-------------------------------------------------------------------------------------------\n",
updated naming
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"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"
subj2
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]
}
],
"source": [
updated naming
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"MIN_TEST_TRIALS = 10\n",
subj2
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"\n",
updated naming
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"results = []\n",
subj2
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"\n",
"for subj in subjects:\n",
" subj_ses = sessions[subj]\n",
"\n",
" for pair in PAIRS:\n",
updated naming
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" 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",
subj2
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" if missing:\n",
updated naming
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" print(f'[{subj}] {pair[\"name\"]}: no session found for {missing} — skipping')\n",
subj2
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" 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",
updated naming
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"\n",
" clf = CSPLDA(n_csp=N_CSP).fit(train['X'], train['y'])\n",
subj2
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" train_acc = (clf.predict(train['X']) == train['y']).mean()\n",
"\n",
updated naming
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" 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",
subj2
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" if len(te['y']) < MIN_TEST_TRIALS or set(np.unique(te['y'])) != {0, 1}:\n",
updated naming
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" print(f'[{subj}] {pair[\"name\"]} / {cond_label}: only {len(te[\"y\"])} clean epochs — '\n",
" f'acc={acc:.3f} (marker-based), skipping EEG metrics')\n",
subj2
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" continue\n",
updated naming
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"\n",
subj2
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" 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",
updated naming
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" acc=acc,\n",
" amp=res['amp'], fisher=res['fisher'], mu_snr=snr_s,\n",
subj2
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" margin=res['margin'], y_test=res['y'], pred=res['pred'],\n",
" ))\n",
"\n",
updated naming
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"hdr = (f'{\"Subj\":<5} {\"Pair\":<28} {\"Cond\":<6} {\"n\":>4} '\n",
" f'{\"trainAcc\":>9} {\"mkAcc\":>7} {\"|marg|\":>8} {\"Fisher\":>8} {\"muSNR\":>8}')\n",
subj2
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"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}')"
]
cd
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},
{
"cell_type": "markdown",
"id": "2ab81600",
"metadata": {},
subj2
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"source": [
"---\n",
"## Figure 1 — Per-metric comparison (FES vs NOFES)"
]
cd
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},
{
"cell_type": "code",
subj2
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"execution_count": 8,
cd
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"id": "d53e63b9",
subj2
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"metadata": {
"execution": {
updated naming
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"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"
subj2
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}
},
"outputs": [
{
"data": {
updated naming
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"image/png": "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
subj2
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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')"
]
cd
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},
{
"cell_type": "markdown",
"id": "248740bd",
"metadata": {},
subj2
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"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|."
]
cd
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},
{
"cell_type": "code",
subj2
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"execution_count": 9,
cd
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"id": "393042a0",
subj2
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"metadata": {
"execution": {
updated naming
2026-04-21 22:12:59 -05:00
"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"
subj2
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}
},
"outputs": [
{
"data": {
updated naming
2026-04-21 22:12:59 -05:00
"image/png": "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
subj2
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"text/plain": [
updated naming
2026-04-21 22:12:59 -05:00
"<Figure size 1440x1536 with 8 Axes>"
subj2
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]
},
"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')"
]
cd
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},
{
"cell_type": "markdown",
"id": "fcb6d19d",
"metadata": {},
subj2
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"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."
]
cd
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},
{
"cell_type": "code",
subj2
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"execution_count": 10,
cd
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"id": "75df404b",
subj2
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"metadata": {
"execution": {
updated naming
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"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"
subj2
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}
},
"outputs": [
{
"data": {
updated naming
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"image/png": "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
subj2
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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')"
]
cd
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},
{
"cell_type": "markdown",
"id": "b3db60ba",
"metadata": {},
"source": [
"---\n",
"## Summary Statistics"
]
},
{
"cell_type": "code",
subj2
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"execution_count": 11,
cd
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"id": "cf55268e",
subj2
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"metadata": {
"execution": {
updated naming
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"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"
subj2
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}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
updated naming
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"=== Aggregate across 7 complete (subject × pair) comparisons ===\n",
subj2
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"\n",
"Metric FES (mean ± sd) NOFES (mean ± sd) paired Δ\n",
"---------------------------------------------------------------------------------------\n",
updated naming
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"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",
subj2
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"\n",
updated naming
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" 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"
subj2
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]
}
],
"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})')"
]
cd
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}
],
"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"
}
},
"nbformat": 4,
"nbformat_minor": 5
subj2
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}
Reference in New Issue Copy Permalink