ECE374N/HW-3/hw3_longitudinal_plasticity.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HW-III: Longitudinal MI Training and Plasticity\n",
    "## ECE-374N/385J Neural Engineering — Spring 2024\n",
    "\n",
    "Analysis of neuro-plastic effects of longitudinal Motor Imagery (MI) BCI training for two healthy subjects.\n",
    "- 7 sessions each: 1 offline + 6 online\n",
    "- 3 runs per session, 20 trials per run\n",
    "- 32-channel EEG, fs = 512 Hz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup & Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "30ca5d4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup complete.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy.io as sio\n",
    "from scipy import signal, stats\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "from matplotlib.patches import Circle\n",
    "import pymannkendall as mk\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# MNE for topoplots\n",
    "import mne\n",
    "from mne.viz import plot_topomap\n",
    "mne.set_log_level('WARNING')\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.dpi'] = 120\n",
    "plt.rcParams['font.size'] = 10\n",
    "\n",
    "DATA_DIR = 'HW_3_sp23_longitudinalTraining_Plasticity_ECE374N-385J_Neural_Eng/'\n",
    "print('Setup complete.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "04600b31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fs = 512 Hz\n",
      "Channels (32): ['Fp1' 'Fpz' 'Fp2' 'F7' 'F3' 'Fz' 'F4' 'F8' 'FC5' 'FC1' 'FC2' 'FC6' 'M1'\n",
      " 'T7' 'C3' 'Cz' 'C4' 'T8' 'M2' 'CP5' 'CP1' 'CP2' 'CP6' 'P7' 'P3' 'Pz' 'P4'\n",
      " 'P8' 'POz' 'O1' 'Oz' 'O2']\n",
      "Subjects loaded.\n",
      "\n",
      "Subject 1 | Online Session 1 | Run 1 — Event sequence:\n",
      "  #       POS (sample)       TYP\n",
      "  ----- --------------  --------\n",
      "  0               6922     32766\n",
      "  1               7183      1000\n",
      "  2               8653       768\n",
      "  3               9681       769\n",
      "  4              10706      7691\n",
      "  5              10938      7692\n",
      "  6              11710      1000\n",
      "  7              12936       768\n",
      "  8              13971       770\n",
      "  9              14998      7701\n",
      "  10             15324      7702\n",
      "  11             16097      1000\n",
      "  12             17186       768\n",
      "  13             18218       769\n",
      "  14             19243      7691\n",
      "  15             21083      7692\n",
      "  16             21861      1000\n",
      "  17             23325       768\n",
      "  18             24359       770\n",
      "  19             25384      7701\n",
      "  20             27483      7703\n",
      "  21             28256      1000\n",
      "  22             29559       768\n",
      "  23             30595       770\n",
      "  24             31621      7701\n",
      "  25             32476      7703\n",
      "  26             33249      1000\n",
      "  27             34367       768\n",
      "  28             35399       769\n",
      "  29             36425      7691\n",
      "  30             40022      7692\n",
      "  31             40800      1000\n",
      "  32             42312       768\n",
      "  33             43340       769\n",
      "  34             44367      7691\n",
      "  35             47611      7692\n",
      "  36             48389      1000\n",
      "  37             49764       768\n",
      "  38             50795       770\n",
      "  39             51820      7701\n",
      "  40             53306      7703\n",
      "  41             54084      1000\n",
      "  42             55161       768\n",
      "  43             56189       770\n",
      "  44             57214      7701\n",
      "  45             59802      7703\n",
      "  46             60577      1000\n",
      "  47             62050       768\n",
      "  48             63078       770\n",
      "  49             64104      7701\n",
      "  50             66621      7703\n",
      "  51             67400      1000\n",
      "  52             68635       768\n",
      "  53             69662       769\n",
      "  54             70688      7691\n",
      "  55             71708      7692\n",
      "  56             72486      1000\n",
      "  57             73741       768\n",
      "  58             74776       770\n",
      "  59             75803      7701\n",
      "  60             78173      7703\n",
      "  61             78949      1000\n",
      "  62             80456       768\n",
      "  63             81488       769\n",
      "  64             82515      7691\n",
      "  65             85560      7693\n",
      "  66             86334      1000\n",
      "  67             87487       768\n",
      "  68             88519       769\n",
      "  69             89545      7691\n",
      "  70             93152      7692\n",
      "  71             93926      1000\n",
      "  72             95056       768\n",
      "  73             96084       769\n",
      "  74             97109      7691\n",
      "  75             97694      7693\n",
      "  76             98467      1000\n",
      "  77             99977       768\n",
      "  78            101009       770\n",
      "  79            102035      7701\n",
      "  80            102973      7702\n",
      "  81            103750      1000\n",
      "  82            105135       768\n",
      "  83            106166       770\n",
      "  84            107193      7701\n",
      "  85            110782      7702\n",
      "  86            111556      1000\n",
      "  87            112982       768\n",
      "  88            114010       770\n",
      "  89            115035      7701\n",
      "  90            116185      7703\n",
      "  91            116957      1000\n",
      "  92            118476       768\n",
      "  93            119510       769\n",
      "  94            120537      7691\n",
      "  95            122398      7693\n",
      "  96            123177      1000\n",
      "  97            124555       768\n",
      "  98            125590       769\n",
      "  99            126618      7691\n",
      "  100           128381      7693\n",
      "  101           129155     32766\n"
     ]
    }
   ],
   "source": [
    "# ── Load subject data ───────────────────────────────────────────────────────\n",
    "\n",
    "def load_subject(mat_path):\n",
    "    \"\"\"Load a subject .mat file and return the struct.\"\"\"\n",
    "    data = sio.loadmat(mat_path, squeeze_me=True, struct_as_record=False)\n",
    "    key = [k for k in data if not k.startswith('_')][0]\n",
    "    return data[key]\n",
    "\n",
    "subj1 = load_subject(DATA_DIR + 'subj1.mat')\n",
    "subj2 = load_subject(DATA_DIR + 'subj2.mat')\n",
    "subjects = [subj1, subj2]\n",
    "subj_names = ['Subject-1', 'Subject-2']\n",
    "N_SESSIONS = 6   # online sessions\n",
    "N_RUNS = 3\n",
    "N_TRIALS = 20    # per run\n",
    "FS = subj1.offline.run[0].header.fs  # 512 Hz\n",
    "\n",
    "# ── Load channel locations ──────────────────────────────────────────────────\n",
    "loc_data = sio.loadmat('ch32Locations.mat', squeeze_me=True, struct_as_record=False)\n",
    "ch_locs = loc_data['ch32Locations']\n",
    "CH_LABELS = np.array([ch_locs[i].labels for i in range(32)])\n",
    "# 3-D Cartesian (EEGLAB convention: X=nose, Y=left, Z=up)\n",
    "X_eeg = np.array([ch_locs[i].X for i in range(32)])\n",
    "Y_eeg = np.array([ch_locs[i].Y for i in range(32)])\n",
    "# Convert to 2-D topomap coords: mne expects (right, front)\n",
    "pos2d = np.column_stack([-Y_eeg, X_eeg])\n",
    "pos2d = pos2d / np.max(np.linalg.norm(pos2d, axis=1))  # normalise to unit circle\n",
    "\n",
    "print(f'fs = {FS} Hz')\n",
    "print(f'Channels ({len(CH_LABELS)}):', CH_LABELS)\n",
    "print('Subjects loaded.')\n",
    "\n",
    "# ── Event sequence: Subject 1, Session 1, Run 1 ─────────────────────────────\n",
    "typs_s1_s1_r1 = subj1.online[0].run[0].header.triggers.TYP\n",
    "pos_s1_s1_r1  = subj1.online[0].run[0].header.triggers.POS\n",
    "print('\\nSubject 1 | Online Session 1 | Run 1 — Event sequence:')\n",
    "print(f'  {\"#\":<5} {\"POS (sample)\":>14}  {\"TYP\":>8}')\n",
    "print(f'  {\"-\"*5} {\"-\"*14}  {\"-\"*8}')\n",
    "for i, (p, t) in enumerate(zip(pos_s1_s1_r1, typs_s1_s1_r1)):\n",
    "    print(f'  {i:<5} {p:>14}  {t:>8}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8f067be",
   "metadata": {},
   "source": [
    "---\n",
    "# Section 2.1 — BCI Command Delivery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b0811a77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Subject-1\n",
      "  ------------------------------------------------\n",
      "                  Run 1    Run 2    Run 3     Mean\n",
      "  ------------------------------------------------\n",
      "  Session 1       55.0%    45.0%    55.0%    51.7%   acc\n",
      "                  15.0%    30.0%    25.0%    23.3%   timeout\n",
      "  Session 2       60.0%    50.0%    45.0%    51.7%   acc\n",
      "                  35.0%    25.0%    50.0%    36.7%   timeout\n",
      "  Session 3       45.0%    65.0%    60.0%    56.7%   acc\n",
      "                  35.0%    35.0%    40.0%    36.7%   timeout\n",
      "  Session 4       50.0%    60.0%    30.0%    46.7%   acc\n",
      "                  45.0%    40.0%    65.0%    50.0%   timeout\n",
      "  Session 5       65.0%    80.0%    70.0%    71.7%   acc\n",
      "                  25.0%    20.0%    15.0%    20.0%   timeout\n",
      "  Session 6       80.0%    50.0%    35.0%    55.0%   acc\n",
      "                  15.0%    40.0%    45.0%    33.3%   timeout\n",
      "  ------------------------------------------------\n",
      "  Overall acc     55.6%\n",
      "  Overall t/o     33.3%\n",
      "\n",
      "Subject-2\n",
      "  ------------------------------------------------\n",
      "                  Run 1    Run 2    Run 3     Mean\n",
      "  ------------------------------------------------\n",
      "  Session 1       40.0%    45.0%    50.0%    45.0%   acc\n",
      "                  25.0%     0.0%     0.0%     8.3%   timeout\n",
      "  Session 2        5.0%    25.0%    30.0%    20.0%   acc\n",
      "                  65.0%    60.0%    45.0%    56.7%   timeout\n",
      "  Session 3       60.0%    55.0%    20.0%    45.0%   acc\n",
      "                  25.0%    35.0%    70.0%    43.3%   timeout\n",
      "  Session 4       25.0%    45.0%    35.0%    35.0%   acc\n",
      "                  75.0%    55.0%    65.0%    65.0%   timeout\n",
      "  Session 5       75.0%    55.0%    35.0%    55.0%   acc\n",
      "                  25.0%    45.0%    65.0%    45.0%   timeout\n",
      "  Session 6       75.0%    45.0%    50.0%    56.7%   acc\n",
      "                  25.0%    55.0%    50.0%    43.3%   timeout\n",
      "  ------------------------------------------------\n",
      "  Overall acc     42.8%\n",
      "  Overall t/o     43.6%\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x1291.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved table_accuracy_timeout.png\n"
     ]
    }
   ],
   "source": [
    "TIMEOUT_THRESH_SAMP = int(6.9 * FS)  # 6.9 s in samples\n",
    "\n",
    "accuracy     = []   # 2 x 6 x 3\n",
    "timeout_rate = []   # 2 x 6 x 3\n",
    "\n",
    "for subj in [subj1, subj2]:\n",
    "    subj_session_run_accuracy     = []\n",
    "    subj_session_run_timeout_rate = []\n",
    "    for session in subj.online:\n",
    "        session_run_accuracy     = []\n",
    "        session_run_timeout_rate = []\n",
    "        for run in session.run:\n",
    "            trial_start_samp = 0\n",
    "            run_hits     = 0\n",
    "            run_timeouts = 0\n",
    "            run_total    = 0\n",
    "            for typ, pos in zip(run.header.triggers.TYP, run.header.triggers.POS):\n",
    "                if (typ == 7691) or (typ == 7701):\n",
    "                    trial_start_samp = pos\n",
    "                elif (typ == 7692) or (typ == 7702):\n",
    "                    run_total += 1\n",
    "                    if (pos - trial_start_samp) >= TIMEOUT_THRESH_SAMP:\n",
    "                        run_timeouts += 1\n",
    "                elif (typ == 7693) or (typ == 7703):\n",
    "                    run_hits  += 1\n",
    "                    run_total += 1\n",
    "            session_run_accuracy.append(run_hits     / run_total)\n",
    "            session_run_timeout_rate.append(run_timeouts / run_total)\n",
    "        subj_session_run_accuracy.append(session_run_accuracy)\n",
    "        subj_session_run_timeout_rate.append(session_run_timeout_rate)\n",
    "    accuracy.append(subj_session_run_accuracy)\n",
    "    timeout_rate.append(subj_session_run_timeout_rate)\n",
    "\n",
    "accuracy     = np.array(accuracy)      # (2, 6, 3)\n",
    "timeout_rate = np.array(timeout_rate)  # (2, 6, 3)\n",
    "\n",
    "# ── Pretty-print ─────────────────────────────────────────────────────────────\n",
    "header = f\"  {\"\":<12}  {\"Run 1\":>7}  {\"Run 2\":>7}  {\"Run 3\":>7}  {\"Mean\":>7}\"\n",
    "sep = \"  \" + \"-\" * (len(header) - 2)\n",
    "for si, name in enumerate([\"Subject-1\", \"Subject-2\"]):\n",
    "    print(f\"{name}\")\n",
    "    print(sep)\n",
    "    print(header)\n",
    "    print(sep)\n",
    "    for sess_idx in range(6):\n",
    "        accs = accuracy[si, sess_idx]\n",
    "        tos  = timeout_rate[si, sess_idx]\n",
    "        runs_acc = \"\".join([f\"  {a*100:6.1f}%\" for a in accs])\n",
    "        runs_to  = \"\".join([f\"  {t*100:6.1f}%\" for t in tos])\n",
    "        print(f\"  Session {sess_idx+1:<4}{runs_acc}  {accs.mean()*100:6.1f}%   acc\")\n",
    "        print(f\"  {\"\":<12}{runs_to}  {tos.mean()*100:6.1f}%   timeout\")\n",
    "    print(sep)\n",
    "    print(f\"  {\"Overall acc\":<12}{\"\"*24}  {accuracy[si].mean()*100:6.1f}%\")\n",
    "    print(f\"  {\"Overall t/o\":<12}{\"\"*24}  {timeout_rate[si].mean()*100:6.1f}%\")\n",
    "    print()\n",
    "\n",
    "# ── Save table as PNG ─────────────────────────────────────────────────────────\n",
    "col_labels = ['Run 1', 'Run 2', 'Run 3', 'Mean']\n",
    "row_labels, cell_vals, row_colors = [], [], []\n",
    "for si, name in enumerate(['Subject-1', 'Subject-2']):\n",
    "    if si > 0:\n",
    "        row_labels.append(''); cell_vals.append([''] * 4); row_colors.append(['#ffffff'] * 4)\n",
    "    row_labels.append(name); cell_vals.append([''] * 4); row_colors.append(['#d0d8e8'] * 4)\n",
    "    for sess_idx in range(6):\n",
    "        accs = accuracy[si, sess_idx]\n",
    "        tos  = timeout_rate[si, sess_idx]\n",
    "        row_labels.append(f'  Session {sess_idx+1}  acc')\n",
    "        cell_vals.append([f'{a*100:.1f}%' for a in accs] + [f'{accs.mean()*100:.1f}%'])\n",
    "        bg = '#f7f7f7' if sess_idx % 2 == 0 else '#ffffff'\n",
    "        row_colors.append([bg] * 4)\n",
    "        row_labels.append(f'  Session {sess_idx+1}  t/o')\n",
    "        cell_vals.append([f'{t*100:.1f}%' for t in tos] + [f'{tos.mean()*100:.1f}%'])\n",
    "        row_colors.append([bg] * 4)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, len(row_labels) * 0.38 + 0.5))\n",
    "ax.axis('off')\n",
    "tbl = ax.table(\n",
    "    cellText=cell_vals,\n",
    "    rowLabels=row_labels,\n",
    "    colLabels=col_labels,\n",
    "    cellLoc='center',\n",
    "    rowLoc='left',\n",
    "    loc='center',\n",
    ")\n",
    "tbl.auto_set_font_size(False)\n",
    "tbl.set_fontsize(10)\n",
    "tbl.scale(1, 1.4)\n",
    "\n",
    "# Apply row background colours\n",
    "for (row, col), cell in tbl.get_celld().items():\n",
    "    if row == 0:\n",
    "        cell.set_facecolor('#4472c4')\n",
    "        cell.set_text_props(color='white', fontweight='bold')\n",
    "    elif col == -1:\n",
    "        cell.set_facecolor('#e8ecf4')\n",
    "    elif row - 1 < len(row_colors):\n",
    "        cell.set_facecolor(row_colors[row - 1][col])\n",
    "    if col == 3:\n",
    "        cell.set_text_props(fontweight='bold')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('table_accuracy_timeout.png', bbox_inches='tight', dpi=150)\n",
    "plt.show()\n",
    "print('Saved table_accuracy_timeout.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a0e66b29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 840x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sessions = range(1, 7)\n",
    "acc_s1 = [sum(r) / len(r) for r in accuracy[0]]\n",
    "acc_s2 = [sum(r) / len(r) for r in accuracy[1]]\n",
    "\n",
    "plt.figure(figsize=(7, 4))\n",
    "plt.plot(sessions, [a * 100 for a in acc_s1], \"o-\", label=\"Subject-1\")\n",
    "plt.plot(sessions, [a * 100 for a in acc_s2], \"s-\", label=\"Subject-2\")\n",
    "plt.axhline(50, color=\"gray\", linestyle=\"--\", linewidth=1, label=\"Chance (50%)\")\n",
    "plt.xlabel(\"Session\")\n",
    "plt.ylabel(\"Accuracy (%)\")\n",
    "plt.title(\"BCI Accuracy per Session\")\n",
    "plt.xticks(sessions)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e177376b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====================================================================\n",
      "PEARSON CORRELATION  —  session number vs accuracy\n",
      "====================================================================\n",
      "  Group           r = +0.590   p = 0.2173  ns   (mean 49.2%)\n",
      "  Subject-1       r = +0.414   p = 0.4144  ns   (mean 55.6%)\n",
      "  Subject-2       r = +0.600   p = 0.2075  ns   (mean 42.8%)\n",
      "\n",
      "====================================================================\n",
      "WILCOXON RANK-SUM  —  sessions 1-3 vs 4-6  (accuracy)\n",
      "====================================================================\n",
      "  Group           U = 126   p = 0.2655  ns   early 45.0%  ->  late 53.3%\n",
      "  Subject-1       U = 32   p = 0.4494  ns   early 53.3%  ->  late 57.8%\n",
      "  Subject-2       U = 28   p = 0.2675  ns   early 36.7%  ->  late 48.9%\n",
      "\n",
      "====================================================================\n",
      "PEARSON CORRELATION  —  session number vs timeout rate\n",
      "====================================================================\n",
      "  Group           r = +0.334   p = 0.5175  ns   (mean 38.5%)\n",
      "  Subject-1       r = +0.066   p = 0.9007  ns   (mean 33.3%)\n",
      "  Subject-2       r = +0.446   p = 0.3750  ns   (mean 43.6%)\n",
      "\n",
      "====================================================================\n",
      "WILCOXON RANK-SUM  —  sessions 1-3 vs 4-6  (timeout rate)\n",
      "====================================================================\n",
      "  Group           U = 124   p = 0.2263  ns   early 34.2%  ->  late 42.8%\n",
      "  Subject-1       U = 37   p = 0.7893  ns   early 32.2%  ->  late 34.4%\n",
      "  Subject-2       U = 28   p = 0.2655  ns   early 36.1%  ->  late 51.1%\n",
      "\n",
      "====================================================================\n",
      "MANN-KENDALL TREND TEST  —  accuracy\n",
      "====================================================================\n",
      "  Group           tau = +0.467   p = 0.2597  ns   trend: no trend\n",
      "  Subject-1       tau = +0.267   p = 0.5661  ns   trend: no trend\n",
      "  Subject-2       tau = +0.467   p = 0.2597  ns   trend: no trend\n",
      "\n",
      "====================================================================\n",
      "MANN-KENDALL TREND TEST  —  timeout rate\n",
      "====================================================================\n",
      "  Group           tau = +0.067   p = 1.0000  ns   trend: no trend\n",
      "  Subject-1       tau = +0.000   p = 1.0000  ns   trend: no trend\n",
      "  Subject-2       tau = +0.200   p = 0.7071  ns   trend: no trend\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import pearsonr, mannwhitneyu\n",
    "\n",
    "sessions = np.arange(1, 7)\n",
    "\n",
    "def sig_stars(p):\n",
    "    return '***' if p < 0.001 else ('**' if p < 0.01 else ('*' if p < 0.05 else 'ns'))\n",
    "\n",
    "# ── Pearson: session number vs accuracy ──────────────────────────────────────\n",
    "print('=' * 68)\n",
    "print('PEARSON CORRELATION  —  session number vs accuracy')\n",
    "print('=' * 68)\n",
    "\n",
    "acc_group_mean = accuracy.mean(axis=(0, 2))          # (6,) mean over subjects & runs\n",
    "r, p = pearsonr(sessions, acc_group_mean)\n",
    "print(f'  {\"Group\":<14}  r = {r:+.3f}   p = {p:.4f}  {sig_stars(p)}'\n",
    "      f'   (mean {acc_group_mean.mean()*100:.1f}%)')\n",
    "\n",
    "for si, name in enumerate(['Subject-1', 'Subject-2']):\n",
    "    sess_mean = accuracy[si].mean(axis=1)            # (6,)\n",
    "    r, p = pearsonr(sessions, sess_mean)\n",
    "    print(f'  {name:<14}  r = {r:+.3f}   p = {p:.4f}  {sig_stars(p)}'\n",
    "          f'   (mean {sess_mean.mean()*100:.1f}%)')\n",
    "\n",
    "# ── Wilcoxon rank-sum: sessions 1-3 vs 4-6, accuracy ────────────────────────\n",
    "print()\n",
    "print('=' * 68)\n",
    "print('WILCOXON RANK-SUM  —  sessions 1-3 vs 4-6  (accuracy)')\n",
    "print('=' * 68)\n",
    "\n",
    "early_g = accuracy[:, :3, :].flatten()\n",
    "late_g  = accuracy[:, 3:, :].flatten()\n",
    "stat, p = mannwhitneyu(early_g, late_g, alternative='two-sided')\n",
    "print(f'  {\"Group\":<14}  U = {stat:.0f}   p = {p:.4f}  {sig_stars(p)}'\n",
    "      f'   early {early_g.mean()*100:.1f}%  ->  late {late_g.mean()*100:.1f}%')\n",
    "\n",
    "for si, name in enumerate(['Subject-1', 'Subject-2']):\n",
    "    early = accuracy[si, :3, :].flatten()\n",
    "    late  = accuracy[si, 3:, :].flatten()\n",
    "    stat, p = mannwhitneyu(early, late, alternative='two-sided')\n",
    "    print(f'  {name:<14}  U = {stat:.0f}   p = {p:.4f}  {sig_stars(p)}'\n",
    "          f'   early {early.mean()*100:.1f}%  ->  late {late.mean()*100:.1f}%')\n",
    "\n",
    "# ── Pearson: session number vs timeout rate ──────────────────────────────────\n",
    "print()\n",
    "print('=' * 68)\n",
    "print('PEARSON CORRELATION  —  session number vs timeout rate')\n",
    "print('=' * 68)\n",
    "\n",
    "to_group_mean = timeout_rate.mean(axis=(0, 2))       # (6,)\n",
    "r, p = pearsonr(sessions, to_group_mean)\n",
    "print(f'  {\"Group\":<14}  r = {r:+.3f}   p = {p:.4f}  {sig_stars(p)}'\n",
    "      f'   (mean {to_group_mean.mean()*100:.1f}%)')\n",
    "\n",
    "for si, name in enumerate(['Subject-1', 'Subject-2']):\n",
    "    sess_mean = timeout_rate[si].mean(axis=1)\n",
    "    r, p = pearsonr(sessions, sess_mean)\n",
    "    print(f'  {name:<14}  r = {r:+.3f}   p = {p:.4f}  {sig_stars(p)}'\n",
    "          f'   (mean {sess_mean.mean()*100:.1f}%)')\n",
    "\n",
    "# ── Wilcoxon rank-sum: sessions 1-3 vs 4-6, timeout rate ────────────────────\n",
    "print()\n",
    "print('=' * 68)\n",
    "print('WILCOXON RANK-SUM  —  sessions 1-3 vs 4-6  (timeout rate)')\n",
    "print('=' * 68)\n",
    "\n",
    "early_g = timeout_rate[:, :3, :].flatten()\n",
    "late_g  = timeout_rate[:, 3:, :].flatten()\n",
    "stat, p = mannwhitneyu(early_g, late_g, alternative='two-sided')\n",
    "print(f'  {\"Group\":<14}  U = {stat:.0f}   p = {p:.4f}  {sig_stars(p)}'\n",
    "      f'   early {early_g.mean()*100:.1f}%  ->  late {late_g.mean()*100:.1f}%')\n",
    "\n",
    "for si, name in enumerate(['Subject-1', 'Subject-2']):\n",
    "    early = timeout_rate[si, :3, :].flatten()\n",
    "    late  = timeout_rate[si, 3:, :].flatten()\n",
    "    stat, p = mannwhitneyu(early, late, alternative='two-sided')\n",
    "    print(f'  {name:<14}  U = {stat:.0f}   p = {p:.4f}  {sig_stars(p)}'\n",
    "          f'   early {early.mean()*100:.1f}%  ->  late {late.mean()*100:.1f}%')\n",
    "\n",
    "# ── Mann-Kendall: trend over sessions, accuracy ───────────────────────────────\n",
    "import pymannkendall as mk\n",
    "\n",
    "print()\n",
    "print('=' * 68)\n",
    "print('MANN-KENDALL TREND TEST  —  accuracy')\n",
    "print('=' * 68)\n",
    "\n",
    "acc_group_mean = accuracy.mean(axis=(0, 2))\n",
    "res = mk.original_test(acc_group_mean)\n",
    "print(f'  {\"Group\":<14}  tau = {res.Tau:+.3f}   p = {res.p:.4f}  {sig_stars(res.p)}   trend: {res.trend}')\n",
    "\n",
    "for si, name in enumerate(['Subject-1', 'Subject-2']):\n",
    "    sess_mean = accuracy[si].mean(axis=1)\n",
    "    res = mk.original_test(sess_mean)\n",
    "    print(f'  {name:<14}  tau = {res.Tau:+.3f}   p = {res.p:.4f}  {sig_stars(res.p)}   trend: {res.trend}')\n",
    "\n",
    "# ── Mann-Kendall: trend over sessions, timeout rate ───────────────────────────\n",
    "print()\n",
    "print('=' * 68)\n",
    "print('MANN-KENDALL TREND TEST  —  timeout rate')\n",
    "print('=' * 68)\n",
    "\n",
    "to_group_mean = timeout_rate.mean(axis=(0, 2))\n",
    "res = mk.original_test(to_group_mean)\n",
    "print(f'  {\"Group\":<14}  tau = {res.Tau:+.3f}   p = {res.p:.4f}  {sig_stars(res.p)}   trend: {res.trend}')\n",
    "\n",
    "for si, name in enumerate(['Subject-1', 'Subject-2']):\n",
    "    sess_mean = timeout_rate[si].mean(axis=1)\n",
    "    res = mk.original_test(sess_mean)\n",
    "    print(f'  {name:<14}  tau = {res.Tau:+.3f}   p = {res.p:.4f}  {sig_stars(res.p)}   trend: {res.trend}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3e5a19d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================================================================\n",
      "WILCOXON RANK-SUM  —  each session vs session 1  (accuracy)\n",
      "                          stat         p  sig   sess1 mean  sess_s mean\n",
      "  --------------------------------------------------------------------\n",
      "  Group                    3.0    0.1562  ns     48.3%  ->    35.8%   [sess 1 vs 2]\n",
      "  Subject-1                3.0    1.0000  ns     51.7%  ->    51.7%\n",
      "  Subject-2                0.0    0.2500  ns     45.0%  ->    20.0%\n",
      "\n",
      "  Group                    8.5    0.7500  ns     48.3%  ->    50.8%   [sess 1 vs 3]\n",
      "  Subject-1                2.0    0.7500  ns     51.7%  ->    56.7%\n",
      "  Subject-2                3.0    1.0000  ns     45.0%  ->    45.0%\n",
      "\n",
      "  Group                    2.0    0.1875  ns     48.3%  ->    40.8%   [sess 1 vs 4]\n",
      "  Subject-1                2.0    0.7500  ns     51.7%  ->    46.7%\n",
      "  Subject-2                0.0    0.5000  ns     45.0%  ->    35.0%\n",
      "\n",
      "  Group                    4.0    0.2188  ns     48.3%  ->    63.3%   [sess 1 vs 5]\n",
      "  Subject-1                0.0    0.2500  ns     51.7%  ->    71.7%\n",
      "  Subject-2                2.0    0.7500  ns     45.0%  ->    55.0%\n",
      "\n",
      "  Group                    2.0    0.3750  ns     48.3%  ->    55.8%   [sess 1 vs 6]\n",
      "  Subject-1                2.0    0.7500  ns     51.7%  ->    55.0%\n",
      "  Subject-2                0.0    1.0000  ns     45.0%  ->    56.7%\n",
      "\n",
      "\n",
      "========================================================================\n",
      "WILCOXON RANK-SUM  —  each session vs session 1  (timeout rate)\n",
      "                          stat         p  sig   sess1 mean  sess_s mean\n",
      "  --------------------------------------------------------------------\n",
      "  Group                    1.0    0.0625  ns     15.8%  ->    46.7%   [sess 1 vs 2]\n",
      "  Subject-1                1.0    0.5000  ns     23.3%  ->    36.7%\n",
      "  Subject-2                0.0    0.2500  ns      8.3%  ->    56.7%\n",
      "\n",
      "  Group                    0.0    0.0625  ns     15.8%  ->    40.0%   [sess 1 vs 3]\n",
      "  Subject-1                0.0    0.2500  ns     23.3%  ->    36.7%\n",
      "  Subject-2                0.0    0.5000  ns      8.3%  ->    43.3%\n",
      "\n",
      "  Group                    0.0    0.0312  *     15.8%  ->    57.5%   [sess 1 vs 4]\n",
      "  Subject-1                0.0    0.2500  ns     23.3%  ->    50.0%\n",
      "  Subject-2                0.0    0.2500  ns      8.3%  ->    65.0%\n",
      "\n",
      "  Group                    3.5    0.3750  ns     15.8%  ->    32.5%   [sess 1 vs 5]\n",
      "  Subject-1                2.5    1.0000  ns     23.3%  ->    20.0%\n",
      "  Subject-2                0.0    0.5000  ns      8.3%  ->    45.0%\n",
      "\n",
      "  Group                    0.0    0.1250  ns     15.8%  ->    38.3%   [sess 1 vs 6]\n",
      "  Subject-1                0.0    0.5000  ns     23.3%  ->    33.3%\n",
      "  Subject-2                0.0    0.5000  ns      8.3%  ->    43.3%\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import wilcoxon\n",
    "\n",
    "# ── Session-vs-session-1 comparison ──────────────────────────────────────────\n",
    "# Paired by run index within subject; group level pools both subjects (6 pairs).\n",
    "# Wilcoxon signed-rank used throughout (matched pairs, non-parametric).\n",
    "\n",
    "for arr, label in [\n",
    "        (accuracy,     'accuracy'),\n",
    "        (timeout_rate, 'timeout rate'),\n",
    "]:\n",
    "    print('=' * 72)\n",
    "    print(f'WILCOXON RANK-SUM  —  each session vs session 1  ({label})')\n",
    "    print(f'  {\"\":20}  {\"stat\":>6}  {\"p\":>8}  sig   sess1 mean  sess_s mean')\n",
    "    print('  ' + '-' * 68)\n",
    "    for sess_idx in range(1, 6):                  # sessions 2-6 (0-indexed 1-5)\n",
    "        # Group level: pool both subjects -> 6 pairs\n",
    "        ref_g = arr[:, 0, :].flatten()            # session 1, all runs, both subj\n",
    "        cmp_g = arr[:, sess_idx, :].flatten()\n",
    "        try:\n",
    "            stat, p = wilcoxon(cmp_g, ref_g)\n",
    "        except ValueError:\n",
    "            stat, p = np.nan, 1.0\n",
    "        print(f'  {\"Group\":<20}  {stat:>6.1f}  {p:>8.4f}  {sig_stars(p)}'\n",
    "              f'   {ref_g.mean()*100:6.1f}%  ->  {cmp_g.mean()*100:6.1f}%'\n",
    "              f'   [sess 1 vs {sess_idx+1}]')\n",
    "\n",
    "        for si, name in enumerate(['Subject-1', 'Subject-2']):\n",
    "            ref_s = arr[si, 0, :]                 # 3 runs from session 1\n",
    "            cmp_s = arr[si, sess_idx, :]          # 3 runs from session s\n",
    "            try:\n",
    "                stat, p = wilcoxon(cmp_s, ref_s)\n",
    "            except ValueError:\n",
    "                stat, p = np.nan, 1.0\n",
    "            print(f'  {name:<20}  {stat:>6.1f}  {p:>8.4f}  {sig_stars(p)}'\n",
    "                  f'   {ref_s.mean()*100:6.1f}%  ->  {cmp_s.mean()*100:6.1f}%')\n",
    "        print()\n",
    "    print()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97f4b056",
   "metadata": {},
   "source": [
    "---\n",
    "# Section 2.2 — PSD Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3a96c27",
   "metadata": {},
   "outputs": [],
   "source": "# ── PSD Feature Extraction via Welch's Method ─────────────────────────────\n# Task period: from MI cue onset (TYP 769/770) to trial-end trigger\n# (TYP 7692/7693 for left, 7702/7703 for right).\n#\n# Frequency bins: 4, 6, 8, ..., 30 Hz  →  14 bins\n# Channels: 32\n# Features per trial: 32 × 14 = 448\n\nFREQ_BINS  = np.arange(4, 32, 2)        # [4, 6, 8, ..., 30] Hz — 14 bins\nN_FREQ     = len(FREQ_BINS)             # 14\nN_CH       = 32\nN_FEATURES = N_CH * N_FREQ             # 448\n\n# Welch parameters: nperseg = FS/2 → frequency resolution = FS/nperseg = 2 Hz\nNPERSEG  = FS // 2                      # 256 samples\nNOVERLAP = NPERSEG // 2                 # 128 samples (50 % overlap)\n\n# ── Trigger constants ────────────────────────────────────────────────────────\nCUE_TYPES = {769, 770}                   # MI cue onset (class label)\nTRIAL_END = {7692, 7693, 7702, 7703}    # hit / timeout / miss\n\n# psd_all[subj_idx][sess_idx][run_idx] → ndarray (n_trials, 448)\n# lbl_all[subj_idx][sess_idx][run_idx] → ndarray (n_trials,)  {769, 770}\npsd_all = []\nlbl_all = []\n\nfor subj in subjects:\n    psd_subj, lbl_subj = [], []\n\n    for sess in subj.online:                     # 6 online sessions\n        psd_sess, lbl_sess = [], []\n\n        for run in sess.run:                     # 3 runs per session\n            typs = run.header.triggers.TYP\n            pos  = run.header.triggers.POS\n            eeg  = run.eeg                       # (n_samples, 32)\n\n            run_feats, run_lbls = [], []\n            cue_samp  = None\n            cue_class = None\n\n            for typ, p in zip(typs, pos):\n                if typ in CUE_TYPES:\n                    cue_samp  = p\n                    cue_class = typ\n                elif typ in TRIAL_END and cue_samp is not None:\n                    seg = eeg[cue_samp:p, :]     # (n_samp, 32)\n\n                    # Ensure segment is long enough for at least one Welch window\n                    nperseg_eff  = min(NPERSEG, len(seg) - 1) if len(seg) <= NPERSEG else NPERSEG\n                    noverlap_eff = nperseg_eff // 2\n\n                    feats = []\n                    for ch in range(N_CH):\n                        f, pxx = signal.welch(seg[:, ch], fs=FS,\n                                              nperseg=nperseg_eff,\n                                              noverlap=noverlap_eff)\n                        # Pick power at each target frequency bin\n                        # (with nperseg=256 & fs=512, f[k] = 2k Hz exactly)\n                        idx = np.searchsorted(f, FREQ_BINS)\n                        feats.append(pxx[idx])\n\n                    run_feats.append(np.concatenate(feats))   # (448,)\n                    run_lbls.append(cue_class)\n                    cue_samp = cue_class = None\n\n            psd_sess.append(np.array(run_feats))   # (n_trials, 448)\n            lbl_sess.append(np.array(run_lbls))    # (n_trials,)\n\n        psd_subj.append(psd_sess)\n        lbl_subj.append(lbl_sess)\n\n    psd_all.append(psd_subj)\n    lbl_all.append(lbl_subj)\n\n# ── Verification ─────────────────────────────────────────────────────────────\nprint(f'Frequency bins ({N_FREQ}): {FREQ_BINS} Hz')\nprint(f'Features per trial: {N_CH} channels × {N_FREQ} bins = {N_FEATURES}')\nprint()\nprint(f'{\"Subject\":<12}  {\"Session\":<10}  {\"Run\":<6}  {\"Trials\":<8}  {\"Feature shape\"}')\nprint('-' * 58)\nfor si, name in enumerate(subj_names):\n    for se in range(6):\n        for ri in range(3):\n            arr = psd_all[si][se][ri]\n            print(f'{name:<12}  Session {se+1:<3}   Run {ri+1}   '\n                  f'{arr.shape[0]:<8}  {arr.shape}')\n\n# Flat view: (total_trials, 448)\npsd_flat = np.vstack([psd_all[si][se][ri]\n                      for si in range(2)\n                      for se in range(6)\n                      for ri in range(3)])\nlbl_flat = np.concatenate([lbl_all[si][se][ri]\n                           for si in range(2)\n                           for se in range(6)\n                           for ri in range(3)])\n\nprint()\nprint(f'psd_flat shape : {psd_flat.shape}   (expected n_trials × 448)')\nprint(f'lbl_flat shape : {lbl_flat.shape}')\nprint(f'Classes        : {np.unique(lbl_flat)}  (769=left, 770=right)')"
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