{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e62e465f",
   "metadata": {},
   "source": [
    "# Neural decoder evaluation across recording days\n",
    "\n",
    "**Daybreak Neural decoder evaluation project · 8 November 2026**\n",
    "\n",
    "This first experiment uses real EEG from participant 1 in BNCI2014_001. We train two simple models to distinguish imagined left-hand, right-hand, feet, and tongue movement. We compare validation accuracy with a confusion-matrix diagnostic from Kim et al., then inspect performance on the second recording day.\n",
    "\n",
    "**Observed result.** LDA reached 61.8% validation accuracy and 57.3% second-day accuracy. Logistic regression reached 60.0% and 59.8%. The validation ordering reversed on day 2. Only seven test predictions separate the models in total correct counts. One participant and 55 validation trials provide a preliminary baseline with substantial uncertainty.\n",
    "\n",
    "**First 45-minute session.** Spend 10 minutes on the protocol and trial-count checks, 15 minutes tracing the feature extraction and model fitting, and 20 minutes interpreting the confusion matrices. Write down which error pairs change between days before changing any model settings.\n",
    "\n",
    "**Development extension.** The sections after the first experiment repeat the fixed pipeline on participants 2–4, quantify uncertainty by resampling entire recording runs, and compare recurring error patterns. Participant 1 remains an illustrative development case. Participants 5–9 are reserved for later evaluation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4f14476",
   "metadata": {},
   "source": [
    "## How the papers inform this experiment\n",
    "\n",
    "Kim et al., *Beyond Accuracy*, propose pairwise Chernoff information and an identification envelope, U_id, from a classifier's confusion matrix. Their 9.6-fold example compares equal validation accuracy with different held-out accuracies and a simulated controller. Their theory assumes a fixed goal, stationary conditionally independent evidence, and an appropriate channel model. The diagnostics here are estimates and have no demonstrated control-performance guarantee.\n",
    "\n",
    "Zayegh and Refai study radio-frequency OFDM decoding. The transferable experimental idea is to compare behavior under changed signal conditions and assess adaptation using separated data. This notebook studies EEG session changes with fixed models.\n",
    "\n",
    "I chose discrete EEG classification for this first exercise to connect directly to the uploaded intent paper. The earlier intracortical motor-regression proposal would need different metrics and data. The present models use simple bandpower features, so this is an independent baseline rather than a reproduction of the paper's models or results.\n",
    "\n",
    "## Protocol fixed before fitting\n",
    "\n",
    "- Subject 1, four classes, 22 EEG channels. The three EOG channels are excluded.\n",
    "- Each prediction uses four seconds of EEG, from 2 to 6 seconds after the trial marker. It becomes available at the end of that window.\n",
    "- Session 1 original trials 1–230 train the models. Trials 231–288 supply validation. Session 2 is evaluated after both models and validation diagnostics have been frozen.\n",
    "- Exclude source-flagged artifact trials within each split. Report the exclusions. This estimates performance on accepted trials.\n",
    "- Fixed features are log10 power at 8–12 Hz and 13–30 Hz on each EEG channel. Fixed models are shrinkage LDA and logistic regression. Scaling is fitted on training trials only.\n",
    "- Inspect ordinary accuracy, balanced accuracy, confusion counts, and validation-only U_id. Balanced accuracy is mean class recall, which handles the unequal retained class counts.\n",
    "- All later model changes need a new development/evaluation plan. These day-2 results have already been examined."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93c300d2",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "Open this file in Jupyter or upload it to Google Colab. The saved outputs can be read immediately. To rerun, install the packages below if needed, then run the code cells in order. The data-loading cells download about 85 MB from the original source and cache it beside the notebook. The first download may take a few minutes.\n",
    "\n",
    "```python\n",
    "%pip install numpy pandas scipy scikit-learn matplotlib\n",
    "```\n",
    "\n",
    "This run used NumPy 2.3.5, pandas 2.2.3, SciPy 1.17.0, and scikit-learn 1.8.0. Version differences can cause small numerical changes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fc7374b",
   "metadata": {},
   "source": [
    "**Execution record.** All code cells were executed in order in a fresh shared Python namespace and the displayed tables and plots were saved. This environment blocks Jupyter kernel sockets, so execution used an in-process runner. The interactive Jupyter interface has not been tested here. The notebook uses ordinary Python cells and includes setup instructions for Jupyter or Colab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "db39a14e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.12.14\n",
      "NumPy 2.3.5 | pandas 2.2.3 | SciPy 1.17.0 | scikit-learn 1.8.0\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import hashlib\n",
    "import urllib.request\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy\n",
    "from scipy.integrate import trapezoid\n",
    "from scipy.io import loadmat\n",
    "from scipy.signal import welch\n",
    "from scipy.optimize import minimize_scalar\n",
    "from scipy.special import logsumexp\n",
    "import sklearn\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, balanced_accuracy_score, confusion_matrix\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display\n",
    "\n",
    "DATA_DIRECTORY = Path('neural_decoder_data')\n",
    "DATA_URL = 'https://bnci-horizon-2020.eu/database/data-sets/001-2014/'\n",
    "CLASS_LABELS = [1, 2, 3, 4]\n",
    "CLASS_NAMES = ['left hand', 'right hand', 'feet', 'tongue']\n",
    "BANDS = {'mu': (8.0, 12.0), 'beta': (13.0, 30.0)}\n",
    "FEATURE_COLUMNS = [\n",
    "    f'log10_{band}_ch{channel:02d}'\n",
    "    for band in BANDS for channel in range(1, 23)\n",
    "]\n",
    "plt.rcParams.update({'font.size': 11, 'axes.spines.top': False,\n",
    "                     'axes.spines.right': False, 'figure.dpi': 120})\n",
    "\n",
    "import sys\n",
    "pd.set_option(\"display.max_columns\", None)\n",
    "pd.set_option(\"display.max_colwidth\", 45)\n",
    "print(\"Python\", sys.version.split()[0])\n",
    "print(\"NumPy\", np.__version__, \"| pandas\", pd.__version__, \"| SciPy\", scipy.__version__, \"| scikit-learn\", sklearn.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0c4da4d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "EXPECTED_SHA256 = {'A01E.mat': '53d415f39c3d7b0c88b894d7b08d99bcdfe855ede63831d3691af1a45607fb62', 'A01T.mat': '054f02e70cf9c4ada1517e9b9864f45407939c1062c6793516585c6f511d0325', 'A02E.mat': 'd63c454005d3a9b41d8440629482e855afc823339bdd0b5721842a7ee9cc7b12', 'A02T.mat': '5ddd5cb520b1692c3ba1363f48d98f58f0e46f3699ee50d749947950fc39db27', 'A03E.mat': 'd4229267ec7624fa8bd3af5cbebac17f415f7c722de6cb676748f8cb3b717d97', 'A03T.mat': '7e731ee8b681d5da6ecb11ae1d4e64b1653c7f15aad5d6b7620b25ce53141e80', 'A04E.mat': '81916dff2c12997974ba50ffc311da006ea66e525010d010765f0047e771c86a', 'A04T.mat': '15850d81b95fc88cc8b9589eb9b713d49fa071e28adaf32d675b3eaa30591d6e'}\n",
    "\n",
    "def download_session(data_directory, filename):\n",
    "    \"\"\"Download a public source file once, with an atomic final rename.\"\"\"\n",
    "    data_directory = Path(data_directory)\n",
    "    data_directory.mkdir(parents=True, exist_ok=True)\n",
    "    destination = data_directory / filename\n",
    "    if not destination.exists():\n",
    "        temporary = destination.with_suffix(\".mat.part\")\n",
    "        with urllib.request.urlopen(DATA_URL + filename, timeout=120) as response:\n",
    "            with temporary.open(\"wb\") as stream:\n",
    "                while chunk := response.read(1024 * 1024):\n",
    "                    stream.write(chunk)\n",
    "        temporary.replace(destination)\n",
    "    return destination\n",
    "\n",
    "def verified_session(filename):\n",
    "    path = download_session(DATA_DIRECTORY, filename)\n",
    "    digest = hashlib.sha256(path.read_bytes()).hexdigest()\n",
    "    if digest != EXPECTED_SHA256[filename]:\n",
    "        raise ValueError(f'{filename} differs from the recorded source. Check the download before proceeding.')\n",
    "    return path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15c0da8f",
   "metadata": {},
   "source": [
    "## 1. Convert recordings into a feature table\n",
    "\n",
    "The MATLAB files contain separate recording runs. Their trial markers use one-based sample numbering. A Welch spectrum estimates power within each complete four-second trial window, separately for each EEG channel. Integrating two frequency bands produces 44 features per trial. No learned transformation is fitted during this step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4968831f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_session_features(path, session, subject=1):\n",
    "    \"\"\"Make one row per trial, retaining source artifact flags and trial order.\n",
    "\n",
    "    BNCI MATLAB trial markers are 1-indexed trial-start sample positions.\n",
    "    Motor imagery starts 2 seconds later and ends at trial-start + 6 seconds.\n",
    "    Only the first 22 channels are EEG. The final three EOG channels are excluded.\n",
    "    \"\"\"\n",
    "    runs = loadmat(path, simplify_cells=True)[\"data\"]\n",
    "    rows = []\n",
    "    session_trial = 0\n",
    "    task_run = 0\n",
    "    for source_run, run in enumerate(runs, start=1):\n",
    "        trial_starts = np.asarray(run[\"trial\"], dtype=int)\n",
    "        if trial_starts.size == 0:\n",
    "            continue\n",
    "        task_run += 1\n",
    "        sample_rate = int(run[\"fs\"])\n",
    "        labels = np.asarray(run[\"y\"], dtype=int)\n",
    "        artifact_flags = np.asarray(run[\"artifacts\"], dtype=int)\n",
    "        assert sample_rate == 250\n",
    "        assert run[\"X\"].shape[1] == 25\n",
    "        assert len(trial_starts) == len(labels) == len(artifact_flags) == 48\n",
    "        assert list(run[\"classes\"]) == CLASS_NAMES\n",
    "        for within_run, (start_sample, label, artifact) in enumerate(\n",
    "            zip(trial_starts, labels, artifact_flags), start=1\n",
    "        ):\n",
    "            session_trial += 1\n",
    "            start = start_sample - 1 + 2 * sample_rate\n",
    "            stop = start_sample - 1 + 6 * sample_rate\n",
    "            epoch = run[\"X\"][start:stop, :22]\n",
    "            assert epoch.shape == (1000, 22)\n",
    "            assert np.isfinite(epoch).all()\n",
    "            frequencies, spectral_density = welch(\n",
    "                epoch,\n",
    "                fs=sample_rate,\n",
    "                window=\"hann\",\n",
    "                nperseg=sample_rate,\n",
    "                noverlap=sample_rate // 2,\n",
    "                detrend=\"constant\",\n",
    "                scaling=\"density\",\n",
    "                axis=0,\n",
    "            )\n",
    "            features = []\n",
    "            for low, high in BANDS.values():\n",
    "                selected = (frequencies >= low) & (frequencies <= high)\n",
    "                power = trapezoid(\n",
    "                    spectral_density[selected], frequencies[selected], axis=0\n",
    "                )\n",
    "                assert np.all(power > 0)\n",
    "                features.extend(np.log10(power))\n",
    "            row = {\n",
    "                \"subject\": subject,\n",
    "                \"session\": session,\n",
    "                \"source_run\": source_run,\n",
    "                \"task_run\": task_run,\n",
    "                \"trial_in_run\": within_run,\n",
    "                \"trial_in_session\": session_trial,\n",
    "                \"label\": int(label),\n",
    "                \"artifact\": int(artifact),\n",
    "            }\n",
    "            row.update(dict(zip(FEATURE_COLUMNS, features)))\n",
    "            rows.append(row)\n",
    "    frame = pd.DataFrame(rows)\n",
    "    assert len(frame) == 288\n",
    "    assert frame.groupby(\"label\").size().to_dict() == {1: 72, 2: 72, 3: 72, 4: 72}\n",
    "    return frame\n",
    "\n",
    "def assign_splits(features):\n",
    "    \"\"\"Define chronological splits before removing source-flagged trials.\"\"\"\n",
    "    result = features.copy()\n",
    "    session_one_trials = (result[\"session\"] == 1).sum()\n",
    "    cutoff = int(np.floor(0.8 * session_one_trials))\n",
    "    result[\"split\"] = \"test\"\n",
    "    session_one = result[\"session\"] == 1\n",
    "    result.loc[session_one, \"split\"] = \"validation\"\n",
    "    result.loc[session_one & (result[\"trial_in_session\"] <= cutoff), \"split\"] = \"train\"\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "28d8b49e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>split</th>\n",
       "      <th>original trials</th>\n",
       "      <th>flagged exclusions</th>\n",
       "      <th>retained trials</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train</td>\n",
       "      <td>230</td>\n",
       "      <td>12</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>validation</td>\n",
       "      <td>58</td>\n",
       "      <td>3</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        split  original trials  flagged exclusions  retained trials\n",
       "0       train              230                  12              218\n",
       "1  validation               58                   3               55"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>validation trials</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>left hand</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right hand</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feet</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tongue</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0       validation trials\n",
       "label                        \n",
       "left hand                  12\n",
       "right hand                 12\n",
       "feet                       18\n",
       "tongue                     13"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "session_one = extract_session_features(verified_session('A01T.mat'), session=1)\n",
    "session_one = assign_splits(session_one)\n",
    "training = session_one.query(\"split == 'train' and artifact == 0\").copy()\n",
    "validation = session_one.query(\"split == 'validation' and artifact == 0\").copy()\n",
    "\n",
    "def trial_counts(frame):\n",
    "    rows = []\n",
    "    for split, group in frame.groupby('split', sort=False):\n",
    "        rows.append({'split': split, 'original trials': len(group),\n",
    "                     'flagged exclusions': int(group['artifact'].sum()),\n",
    "                     'retained trials': int((group['artifact'] == 0).sum())})\n",
    "    return pd.DataFrame(rows)\n",
    "\n",
    "display(trial_counts(session_one))\n",
    "display(pd.crosstab(validation['label'], columns='validation trials').rename(index=dict(zip(CLASS_LABELS, CLASS_NAMES))))\n",
    "assert len(training) == 218 and len(validation) == 55\n",
    "assert training['trial_in_session'].max() < validation['trial_in_session'].min()\n",
    "assert set(training['label']) == set(validation['label']) == set(CLASS_LABELS)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d344d93b",
   "metadata": {},
   "source": [
    "## 2. Train two fixed models and evaluate validation trials\n",
    "\n",
    "Each pipeline learns its standardization from the training partition. The two classifiers receive the same features. The paper reports limited extra predictive value from its diagnostic in some comparisons using the same feature representation, so a negative result here is informative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e1cddbba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy</th>\n",
       "      <th>balanced accuracy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LDA</th>\n",
       "      <td>0.6182</td>\n",
       "      <td>0.6368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic regression</th>\n",
       "      <td>0.6000</td>\n",
       "      <td>0.6143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     accuracy  balanced accuracy\n",
       "model                                           \n",
       "LDA                    0.6182             0.6368\n",
       "Logistic regression    0.6000             0.6143"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "models = {\n",
    "    'LDA': make_pipeline(StandardScaler(), LinearDiscriminantAnalysis(solver='lsqr', shrinkage='auto')),\n",
    "    'Logistic regression': make_pipeline(StandardScaler(), LogisticRegression(C=1.0, solver='lbfgs', max_iter=2000, random_state=0)),\n",
    "}\n",
    "predictions = {}\n",
    "validation_rows = []\n",
    "for name, model in models.items():\n",
    "    model.fit(training[FEATURE_COLUMNS], training['label'])\n",
    "    prediction = model.predict(validation[FEATURE_COLUMNS])\n",
    "    predictions[(name, 'validation')] = prediction\n",
    "    validation_rows.append({\n",
    "        'model': name,\n",
    "        'accuracy': accuracy_score(validation['label'], prediction),\n",
    "        'balanced accuracy': balanced_accuracy_score(validation['label'], prediction),\n",
    "    })\n",
    "validation_scores = pd.DataFrame(validation_rows).set_index('model')\n",
    "display(validation_scores.round(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bf66191",
   "metadata": {},
   "source": [
    "## 3. Add the paper's diagnostic\n",
    "\n",
    "Let C[i,j] be the estimated probability of predicting class j when class i is true. Add one count per cell before row normalization to reduce problems from small sample counts. We fix the observation probability to 1.\n",
    "\n",
    "\\[\n",
    "\\Gamma_{ij}=-\\log\\min_{0\\leq s\\leq 1}\\sum_m C_{im}^{1-s}C_{jm}^{s},\\qquad\n",
    "U_{\\mathrm{id}}=\\frac{1}{K}\\sum_i\\sum_{j\\ne i}\\frac{1}{e^{\\Gamma_{ij}}-1}.\n",
    "\\]\n",
    "\n",
    "Large worst-pair Chernoff information indicates that the hardest pair is more distinguishable under the model. A smaller identification envelope indicates less cumulative identification error under the paper's assumptions. It has no direct interpretation as seconds or clinical benefit. Estimates from 12–18 validation trials per class can be unstable. We therefore also check sensitivity to the smoothing count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6bf576fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>smoothing</th>\n",
       "      <th>weakest pair</th>\n",
       "      <th>gamma_min</th>\n",
       "      <th>U_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LDA</td>\n",
       "      <td>0.5</td>\n",
       "      <td>left hand / feet</td>\n",
       "      <td>0.0973</td>\n",
       "      <td>14.8239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LDA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>left hand / feet</td>\n",
       "      <td>0.0751</td>\n",
       "      <td>20.3510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LDA</td>\n",
       "      <td>2.0</td>\n",
       "      <td>left hand / feet</td>\n",
       "      <td>0.0492</td>\n",
       "      <td>32.7076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>0.5</td>\n",
       "      <td>left hand / right hand</td>\n",
       "      <td>0.0847</td>\n",
       "      <td>16.0802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>1.0</td>\n",
       "      <td>left hand / right hand</td>\n",
       "      <td>0.0615</td>\n",
       "      <td>22.3119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>2.0</td>\n",
       "      <td>left hand / right hand</td>\n",
       "      <td>0.0385</td>\n",
       "      <td>36.1453</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 model  smoothing            weakest pair  gamma_min     U_id\n",
       "0                  LDA        0.5        left hand / feet     0.0973  14.8239\n",
       "1                  LDA        1.0        left hand / feet     0.0751  20.3510\n",
       "2                  LDA        2.0        left hand / feet     0.0492  32.7076\n",
       "3  Logistic regression        0.5  left hand / right hand     0.0847  16.0802\n",
       "4  Logistic regression        1.0  left hand / right hand     0.0615  22.3119\n",
       "5  Logistic regression        2.0  left hand / right hand     0.0385  36.1453"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation balanced-accuracy choice: LDA\n",
      "Validation U_id choice: LDA\n"
     ]
    }
   ],
   "source": [
    "def channel_diagnostics(channel):\n",
    "    \"\"\"Chernoff diagnostics for a strictly positive row-stochastic matrix.\"\"\"\n",
    "    channel = np.asarray(channel, dtype=float)\n",
    "    if channel.ndim != 2 or channel.shape[0] != channel.shape[1]:\n",
    "        raise ValueError('Expected a square confusion-probability matrix.')\n",
    "    if not np.isfinite(channel).all() or (channel <= 0).any():\n",
    "        raise ValueError('Use finite, strictly positive probabilities, for example via Laplace smoothing.')\n",
    "    if not np.allclose(channel.sum(axis=1), 1.0):\n",
    "        raise ValueError('Each row must sum to one.')\n",
    "    class_count = len(channel)\n",
    "    pair_values = []\n",
    "    pairs = []\n",
    "    for first in range(class_count):\n",
    "        for second in range(first + 1, class_count):\n",
    "            log_first = np.log(channel[first])\n",
    "            log_second = np.log(channel[second])\n",
    "            solution = minimize_scalar(\n",
    "                lambda weight: logsumexp((1 - weight) * log_first + weight * log_second),\n",
    "                bounds=(0.0, 1.0), method='bounded', options={'xatol': 1e-12},\n",
    "            )\n",
    "            if not solution.success:\n",
    "                raise RuntimeError('Chernoff optimization did not converge.')\n",
    "            pair_values.append(max(0.0, -float(solution.fun)))\n",
    "            pairs.append((first, second))\n",
    "    pair_values = np.asarray(pair_values)\n",
    "    weakest = int(np.argmin(pair_values))\n",
    "    if pair_values.min() <= 1e-12:\n",
    "        envelope = np.inf\n",
    "    else:\n",
    "        envelope = float(2 * np.sum(1 / np.expm1(pair_values)) / class_count)\n",
    "    return {'gamma_min': float(pair_values.min()), 'U_id': envelope,\n",
    "            'weakest_pair': pairs[weakest]}\n",
    "\n",
    "def validation_diagnostics(true_labels, predicted_labels, smoothing=1.0):\n",
    "    counts = confusion_matrix(true_labels, predicted_labels, labels=CLASS_LABELS)\n",
    "    smoothed = counts + smoothing\n",
    "    channel = smoothed / smoothed.sum(axis=1, keepdims=True)\n",
    "    return channel_diagnostics(channel)\n",
    "\n",
    "# Verify the formula on a symmetric channel with an analytic Chernoff value.\n",
    "symmetric = np.full((4, 4), 0.1)\n",
    "np.fill_diagonal(symmetric, 0.7)\n",
    "analytic_gamma = -np.log(2 * np.sqrt(0.7 * 0.1) + 2 * 0.1)\n",
    "checked = channel_diagnostics(symmetric)\n",
    "assert np.isclose(checked['gamma_min'], analytic_gamma)\n",
    "assert np.isclose(checked['U_id'], 3 / np.expm1(analytic_gamma))\n",
    "assert np.isinf(channel_diagnostics(np.full((4, 4), 0.25))['U_id'])\n",
    "permuted = symmetric[[2, 0, 3, 1]][:, [1, 3, 0, 2]]\n",
    "assert np.isclose(channel_diagnostics(permuted)['U_id'], checked['U_id'])\n",
    "\n",
    "diagnostic_rows = []\n",
    "for name in models:\n",
    "    for smoothing in [0.5, 1.0, 2.0]:\n",
    "        diagnostics = validation_diagnostics(validation['label'], predictions[(name, 'validation')], smoothing)\n",
    "        first, second = diagnostics.pop('weakest_pair')\n",
    "        diagnostic_rows.append({'model': name, 'smoothing': smoothing,\n",
    "                                'weakest pair': f'{CLASS_NAMES[first]} / {CLASS_NAMES[second]}',\n",
    "                                **diagnostics})\n",
    "diagnostics_table = pd.DataFrame(diagnostic_rows)\n",
    "display(diagnostics_table.round(4))\n",
    "\n",
    "# Record both selections using validation only, before the next data-loading cell.\n",
    "accuracy_choice = validation_scores['balanced accuracy'].idxmax()\n",
    "primary_diagnostics = diagnostics_table.query('smoothing == 1.0').set_index('model')\n",
    "information_choice = primary_diagnostics['U_id'].idxmin()\n",
    "print('Validation balanced-accuracy choice:', accuracy_choice)\n",
    "print('Validation U_id choice:', information_choice)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "195a3ae6",
   "metadata": {},
   "source": [
    "## 4. Evaluate the frozen models on the second recording day\n",
    "\n",
    "The fitted models, feature definitions, normalization, and validation diagnostics remain frozen for this evaluation. Both candidates are shown so the earlier choices can be audited. Accuracy on this second day is a classification outcome. A test of the paper's proposed relationship with sequential task cost would require a separate, specified controller experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "68f7b3e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>split</th>\n",
       "      <th>original trials</th>\n",
       "      <th>flagged exclusions</th>\n",
       "      <th>retained trials</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>288</td>\n",
       "      <td>7</td>\n",
       "      <td>281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  split  original trials  flagged exclusions  retained trials\n",
       "0  test              288                   7              281"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>validation accuracy</th>\n",
       "      <th>day 2 accuracy</th>\n",
       "      <th>validation balanced accuracy</th>\n",
       "      <th>day 2 balanced accuracy</th>\n",
       "      <th>validation U_id</th>\n",
       "      <th>day 2 correct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LDA</th>\n",
       "      <td>0.6182</td>\n",
       "      <td>0.5730</td>\n",
       "      <td>0.6368</td>\n",
       "      <td>0.5731</td>\n",
       "      <td>20.3510</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic regression</th>\n",
       "      <td>0.6000</td>\n",
       "      <td>0.5979</td>\n",
       "      <td>0.6143</td>\n",
       "      <td>0.5978</td>\n",
       "      <td>22.3119</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     validation accuracy  day 2 accuracy  \\\n",
       "model                                                      \n",
       "LDA                               0.6182          0.5730   \n",
       "Logistic regression               0.6000          0.5979   \n",
       "\n",
       "                     validation balanced accuracy  day 2 balanced accuracy  \\\n",
       "model                                                                        \n",
       "LDA                                        0.6368                   0.5731   \n",
       "Logistic regression                        0.6143                   0.5978   \n",
       "\n",
       "                     validation U_id  day 2 correct  \n",
       "model                                                \n",
       "LDA                          20.3510            161  \n",
       "Logistic regression          22.3119            168  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "session_two = extract_session_features(verified_session('A01E.mat'), session=2)\n",
    "session_two['split'] = 'test'\n",
    "test = session_two.query('artifact == 0').copy()\n",
    "display(trial_counts(session_two))\n",
    "assert len(test) == 281 and set(test['label']) == set(CLASS_LABELS)\n",
    "\n",
    "comparison_rows = []\n",
    "for name, model in models.items():\n",
    "    prediction = model.predict(test[FEATURE_COLUMNS])\n",
    "    predictions[(name, 'test')] = prediction\n",
    "    comparison_rows.append({\n",
    "        'model': name,\n",
    "        'validation accuracy': validation_scores.loc[name, 'accuracy'],\n",
    "        'day 2 accuracy': accuracy_score(test['label'], prediction),\n",
    "        'validation balanced accuracy': validation_scores.loc[name, 'balanced accuracy'],\n",
    "        'day 2 balanced accuracy': balanced_accuracy_score(test['label'], prediction),\n",
    "        'validation U_id': primary_diagnostics.loc[name, 'U_id'],\n",
    "        'day 2 correct': int(np.sum(prediction == test['label'].to_numpy())),\n",
    "    })\n",
    "comparison = pd.DataFrame(comparison_rows).set_index('model')\n",
    "with pd.option_context('display.max_columns', None):\n",
    "    display(comparison.round(4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b485c1d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 4.4), layout='constrained')\n",
    "positions = np.arange(len(comparison))\n",
    "for offset, column, label, color in [\n",
    "    (-0.17, 'validation accuracy', 'Day 1 validation · 55 trials', '#237b8d'),\n",
    "    (0.17, 'day 2 accuracy', 'Day 2 test · 281 trials', '#bc6227'),\n",
    "]:\n",
    "    values = 100 * comparison[column].to_numpy()\n",
    "    bars = ax.bar(positions + offset, values, width=0.32, color=color, label=label)\n",
    "    ax.bar_label(bars, labels=[f'{value:.1f}%' for value in values], padding=3)\n",
    "ax.axhline(25, color='#68737d', linestyle='--', label='Uniform random guessing · 25% expected')\n",
    "ax.set(xticks=positions, xticklabels=comparison.index, ylim=(0, 100),\n",
    "       ylabel='Correct predictions (%)', title='The ordering reverses on the second day\\nBNCI2014_001 · participant 1 · accepted trials')\n",
    "ax.legend(frameon=False, loc='upper right', fontsize=9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2ffc44d",
   "metadata": {},
   "source": [
    "LDA classified 34 of 55 validation trials correctly, compared with 33 for logistic regression. On day 2 the respective counts were 161 and 168 of 281. The validation gap is one trial. Sampling variation, trial composition, and session changes are all plausible contributors. These observations establish a working evaluation pipeline and do not establish a statistically reliable model advantage or a causal explanation of drift."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6714e2fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, figsize=(10, 8.6), layout='constrained')\n",
    "for row, name in enumerate(models):\n",
    "    for column, (split, frame) in enumerate([('validation', validation), ('test', test)]):\n",
    "        ax = axes[row, column]\n",
    "        counts = confusion_matrix(frame['label'], predictions[(name, split)], labels=CLASS_LABELS)\n",
    "        rates = counts / counts.sum(axis=1, keepdims=True)\n",
    "        ax.imshow(rates, vmin=0, vmax=1, cmap='Blues')\n",
    "        for actual in range(4):\n",
    "            for predicted in range(4):\n",
    "                ax.text(predicted, actual, f'{counts[actual, predicted]}\\n{rates[actual, predicted]:.0%}',\n",
    "                        ha='center', va='center', color='white' if rates[actual, predicted] > 0.55 else '#182838', fontsize=10)\n",
    "        ax.set(xticks=range(4), yticks=range(4), xticklabels=CLASS_NAMES, yticklabels=CLASS_NAMES,\n",
    "               xlabel='Predicted imagery class', ylabel='Actual imagery class',\n",
    "               title=f'{name} · {split} · n={len(frame)}')\n",
    "        ax.tick_params(axis='x', labelrotation=25)\n",
    "fig.suptitle('Counts and row percentages reveal which movements are confused\\nParticipant 1 · all four matrices use the same color scale', fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78b2f932",
   "metadata": {},
   "source": [
    "## Inspect one prediction\n",
    "\n",
    "This cell implements the walkthrough discussed in the conversation. Change the row position to inspect another accepted day-two trial from participant 1. The reported probabilities are estimates from the fitted models. Calibration of those probabilities is outside the present analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f8ceaad9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LDA</th>\n",
       "      <th>Logistic regression</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>left hand</th>\n",
       "      <td>0.826</td>\n",
       "      <td>0.861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right hand</th>\n",
       "      <td>0.093</td>\n",
       "      <td>0.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feet</th>\n",
       "      <td>0.034</td>\n",
       "      <td>0.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tongue</th>\n",
       "      <td>0.048</td>\n",
       "      <td>0.032</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              LDA  Logistic regression\n",
       "left hand   0.826                0.861\n",
       "right hand  0.093                0.067\n",
       "feet        0.034                0.040\n",
       "tongue      0.048                0.032"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual instructed movement: left hand\n"
     ]
    }
   ],
   "source": [
    "trial = test.iloc[[0]]\n",
    "features = trial[FEATURE_COLUMNS]\n",
    "actual_label = int(trial['label'].iloc[0])\n",
    "print('Actual instructed movement:', CLASS_NAMES[actual_label - 1])\n",
    "probabilities = {}\n",
    "for name, model in models.items():\n",
    "    probabilities[name] = model.predict_proba(features)[0]\n",
    "display(pd.DataFrame(probabilities, index=CLASS_NAMES).round(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f7ac7ea",
   "metadata": {},
   "source": [
    "## Development analysis on participants 2 through 4\n",
    "\n",
    "We repeat the same feature extraction, trial split, artifact exclusions, and fixed classifiers separately for each person. This measures transfer between days within a participant. The analysis design uses participants 1–4 for development and reserves 5–9 for later evaluation. This split was chosen after seeing participant 1, so the present results are exploratory.\n",
    "\n",
    "All displayed counts refer to accepted trials unless explicitly marked as original counts. The source describes a shorter EOG calibration block for participant 4, which our loader handles by skipping runs without task trials.\n",
    "\n",
    "The first rerun of this section downloads six additional files, about 260 MB. Each source file is checked against its recorded SHA-256 digest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ff67a5f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import clone\n",
    "\n",
    "\n",
    "def evaluate_participant(subject):\n",
    "    \"\"\"Repeat the fixed two-day protocol, fitting a new model for each person.\"\"\"\n",
    "    frames = [\n",
    "        extract_session_features(verified_session(f'A{subject:02d}{suffix}.mat'), session, subject)\n",
    "        for session, suffix in [(1, 'T'), (2, 'E')]\n",
    "    ]\n",
    "    features = assign_splits(pd.concat(frames, ignore_index=True))\n",
    "    accepted = features.query('artifact == 0')\n",
    "    train_rows = accepted.query(\"split == 'train'\")\n",
    "    validation_rows = accepted.query(\"split == 'validation'\")\n",
    "    test_rows = accepted.query(\"split == 'test'\")\n",
    "    assert set(train_rows['label']) == set(validation_rows['label']) == set(test_rows['label']) == set(CLASS_LABELS)\n",
    "    assert train_rows['trial_in_session'].max() < validation_rows['trial_in_session'].min()\n",
    "    scores, diagnostics, trial_predictions = [], [], []\n",
    "    for name, template in models.items():\n",
    "        fitted_model = clone(template)\n",
    "        fitted_model.fit(train_rows[FEATURE_COLUMNS], train_rows['label'])\n",
    "        validation_prediction = fitted_model.predict(validation_rows[FEATURE_COLUMNS])\n",
    "        for smoothing in [0.5, 1.0, 2.0]:\n",
    "            result = validation_diagnostics(validation_rows['label'], validation_prediction, smoothing)\n",
    "            first, second = result.pop('weakest_pair')\n",
    "            diagnostics.append({'subject': subject, 'model': name, 'smoothing': smoothing,\n",
    "                                'weakest_pair': f'{CLASS_NAMES[first]} / {CLASS_NAMES[second]}', **result})\n",
    "        for split, rows in [('validation', validation_rows), ('test', test_rows)]:\n",
    "            prediction = fitted_model.predict(rows[FEATURE_COLUMNS])\n",
    "            counts = confusion_matrix(rows['label'], prediction, labels=CLASS_LABELS)\n",
    "            scores.append({'subject': subject, 'model': name, 'split': split,\n",
    "                           'n': len(rows), 'correct': int(np.sum(prediction == rows['label'].to_numpy())),\n",
    "                           'accuracy': accuracy_score(rows['label'], prediction),\n",
    "                           'balanced_accuracy': balanced_accuracy_score(rows['label'], prediction),\n",
    "                           'confusion': counts.tolist()})\n",
    "            prediction_rows = rows[['subject', 'split', 'task_run', 'trial_in_session', 'label']].copy()\n",
    "            prediction_rows['model'] = name\n",
    "            prediction_rows['prediction'] = prediction\n",
    "            trial_predictions.append(prediction_rows)\n",
    "    counts = trial_counts(features)\n",
    "    counts.insert(0, 'subject', subject)\n",
    "    return features, pd.DataFrame(scores), pd.DataFrame(diagnostics), pd.concat(trial_predictions), counts\n",
    "\n",
    "\n",
    "def bootstrap_paired_runs(prediction_rows, repetitions=2000, seed=1108):\n",
    "    \"\"\"Resample whole test runs, pairing the two models on the same trials.\n",
    "\n",
    "    Intervals describe variation among the six observed recording runs. With\n",
    "    one test session per person, they do not estimate uncertainty across days\n",
    "    or new people. Model parameters remain fixed throughout resampling.\n",
    "    \"\"\"\n",
    "    test_predictions = prediction_rows.query(\"split == 'test'\")\n",
    "    index_columns = ['task_run', 'trial_in_session', 'label']\n",
    "    paired = test_predictions.pivot(index=index_columns, columns='model', values='prediction').reset_index()\n",
    "    assert paired[['LDA', 'Logistic regression']].notna().all().all()\n",
    "    run_ids = np.sort(paired['task_run'].unique())\n",
    "    assert len(run_ids) == 6\n",
    "    totals = np.zeros((len(run_ids), 4))\n",
    "    correct = np.zeros((len(run_ids), 2, 4))\n",
    "    model_names = ['LDA', 'Logistic regression']\n",
    "    for run_index, run_id in enumerate(run_ids):\n",
    "        run = paired.loc[paired['task_run'] == run_id]\n",
    "        for class_index, label in enumerate(CLASS_LABELS):\n",
    "            rows = run.loc[run['label'] == label]\n",
    "            totals[run_index, class_index] = len(rows)\n",
    "            for model_index, name in enumerate(model_names):\n",
    "                correct[run_index, model_index, class_index] = np.sum(rows[name] == label)\n",
    "    rng = np.random.default_rng(seed)\n",
    "    selected_runs = rng.integers(0, len(run_ids), size=(repetitions, len(run_ids)))\n",
    "    sampled_totals = totals[selected_runs].sum(axis=1)\n",
    "    sampled_correct = correct[selected_runs].sum(axis=1)\n",
    "    assert (sampled_totals > 0).all()\n",
    "    accuracy = sampled_correct.sum(axis=2) / sampled_totals.sum(axis=1)[:, None]\n",
    "    balanced = (sampled_correct / sampled_totals[:, None, :]).mean(axis=2)\n",
    "    rows = []\n",
    "    for index, name in enumerate(model_names):\n",
    "        low, high = np.quantile(accuracy[:, index], [0.025, 0.975])\n",
    "        rows.append({'model': name, 'accuracy_low': float(low), 'accuracy_high': float(high)})\n",
    "    difference = balanced[:, 1] - balanced[:, 0]\n",
    "    low, high = np.quantile(difference, [0.025, 0.975])\n",
    "    return pd.DataFrame(rows), {'difference_low': float(low), 'difference_high': float(high)}\n",
    "\n",
    "\n",
    "def summarize_model_choices(scores, diagnostics):\n",
    "    \"\"\"Record validation selections and the observed second-day ordering.\"\"\"\n",
    "    rows = []\n",
    "    for subject in sorted(scores['subject'].unique()):\n",
    "        participant = scores.loc[scores['subject'] == subject]\n",
    "        val = participant.query(\"split == 'validation'\").set_index('model')\n",
    "        test_scores = participant.query(\"split == 'test'\").set_index('model')\n",
    "        diagnostic = diagnostics.query('subject == @subject and smoothing == 1.0').set_index('model')\n",
    "        accuracy_choice = val['balanced_accuracy'].idxmax()\n",
    "        information_choice = diagnostic['U_id'].idxmin()\n",
    "        observed_choice = test_scores['balanced_accuracy'].idxmax()\n",
    "        rows.append({'subject': subject, 'validation_BA_choice': accuracy_choice,\n",
    "                     'validation_U_choice': information_choice, 'observed_day2_BA_choice': observed_choice,\n",
    "                     'day2_BA_difference_LR_minus_LDA': test_scores.loc['Logistic regression', 'balanced_accuracy'] - test_scores.loc['LDA', 'balanced_accuracy']})\n",
    "    return pd.DataFrame(rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2d84d849",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>split</th>\n",
       "      <th>original trials</th>\n",
       "      <th>flagged exclusions</th>\n",
       "      <th>retained trials</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>train</td>\n",
       "      <td>230</td>\n",
       "      <td>12</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>validation</td>\n",
       "      <td>58</td>\n",
       "      <td>3</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>test</td>\n",
       "      <td>288</td>\n",
       "      <td>7</td>\n",
       "      <td>281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>train</td>\n",
       "      <td>230</td>\n",
       "      <td>9</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>validation</td>\n",
       "      <td>58</td>\n",
       "      <td>9</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>test</td>\n",
       "      <td>288</td>\n",
       "      <td>5</td>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>230</td>\n",
       "      <td>17</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>validation</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>288</td>\n",
       "      <td>15</td>\n",
       "      <td>273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>train</td>\n",
       "      <td>230</td>\n",
       "      <td>21</td>\n",
       "      <td>209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4</td>\n",
       "      <td>validation</td>\n",
       "      <td>58</td>\n",
       "      <td>5</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4</td>\n",
       "      <td>test</td>\n",
       "      <td>288</td>\n",
       "      <td>60</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    subject       split  original trials  flagged exclusions  retained trials\n",
       "0         1       train              230                  12              218\n",
       "1         1  validation               58                   3               55\n",
       "2         1        test              288                   7              281\n",
       "3         2       train              230                   9              221\n",
       "4         2  validation               58                   9               49\n",
       "5         2        test              288                   5              283\n",
       "6         3       train              230                  17              213\n",
       "7         3  validation               58                   1               57\n",
       "8         3        test              288                  15              273\n",
       "9         4       train              230                  21              209\n",
       "10        4  validation               58                   5               53\n",
       "11        4        test              288                  60              228"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">accuracy</th>\n",
       "      <th colspan=\"2\" halign=\"left\">balanced_accuracy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>split</th>\n",
       "      <th>test</th>\n",
       "      <th>validation</th>\n",
       "      <th>test</th>\n",
       "      <th>validation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subject</th>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>LDA</th>\n",
       "      <td>57.30</td>\n",
       "      <td>61.82</td>\n",
       "      <td>57.31</td>\n",
       "      <td>63.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic regression</th>\n",
       "      <td>59.79</td>\n",
       "      <td>60.00</td>\n",
       "      <td>59.78</td>\n",
       "      <td>61.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>LDA</th>\n",
       "      <td>32.86</td>\n",
       "      <td>36.73</td>\n",
       "      <td>33.22</td>\n",
       "      <td>36.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic regression</th>\n",
       "      <td>33.92</td>\n",
       "      <td>34.69</td>\n",
       "      <td>34.33</td>\n",
       "      <td>34.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>LDA</th>\n",
       "      <td>60.81</td>\n",
       "      <td>61.40</td>\n",
       "      <td>60.62</td>\n",
       "      <td>61.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic regression</th>\n",
       "      <td>60.07</td>\n",
       "      <td>56.14</td>\n",
       "      <td>59.84</td>\n",
       "      <td>56.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">4</th>\n",
       "      <th>LDA</th>\n",
       "      <td>47.37</td>\n",
       "      <td>43.40</td>\n",
       "      <td>47.61</td>\n",
       "      <td>43.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic regression</th>\n",
       "      <td>48.25</td>\n",
       "      <td>41.51</td>\n",
       "      <td>48.59</td>\n",
       "      <td>41.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            accuracy            balanced_accuracy           \n",
       "split                           test validation              test validation\n",
       "subject model                                                               \n",
       "1       LDA                    57.30      61.82             57.31      63.68\n",
       "        Logistic regression    59.79      60.00             59.78      61.43\n",
       "2       LDA                    32.86      36.73             33.22      36.86\n",
       "        Logistic regression    33.92      34.69             34.33      34.78\n",
       "3       LDA                    60.81      61.40             60.62      61.12\n",
       "        Logistic regression    60.07      56.14             59.84      56.24\n",
       "4       LDA                    47.37      43.40             47.61      43.82\n",
       "        Logistic regression    48.25      41.51             48.59      41.60"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>validation_BA_choice</th>\n",
       "      <th>validation_U_choice</th>\n",
       "      <th>observed_day2_BA_choice</th>\n",
       "      <th>day2_BA_difference_LR_minus_LDA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>LDA</td>\n",
       "      <td>LDA</td>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>0.024698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>LDA</td>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>0.011074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>LDA</td>\n",
       "      <td>LDA</td>\n",
       "      <td>LDA</td>\n",
       "      <td>-0.007837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>LDA</td>\n",
       "      <td>LDA</td>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>0.009846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject validation_BA_choice  validation_U_choice observed_day2_BA_choice  \\\n",
       "0        1                  LDA                  LDA     Logistic regression   \n",
       "1        2                  LDA  Logistic regression     Logistic regression   \n",
       "2        3                  LDA                  LDA                     LDA   \n",
       "3        4                  LDA                  LDA     Logistic regression   \n",
       "\n",
       "   day2_BA_difference_LR_minus_LDA  \n",
       "0                         0.024698  \n",
       "1                         0.011074  \n",
       "2                        -0.007837  \n",
       "3                         0.009846  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "participant_results = {}\n",
    "for subject in [1, 2, 3, 4]:\n",
    "    participant_results[subject] = evaluate_participant(subject)\n",
    "\n",
    "all_scores = pd.concat([result[1] for result in participant_results.values()], ignore_index=True)\n",
    "all_diagnostics = pd.concat([result[2] for result in participant_results.values()], ignore_index=True)\n",
    "all_predictions = pd.concat([result[3] for result in participant_results.values()], ignore_index=True)\n",
    "all_counts = pd.concat([result[4] for result in participant_results.values()], ignore_index=True)\n",
    "choices = summarize_model_choices(all_scores, all_diagnostics)\n",
    "display(all_counts)\n",
    "\n",
    "score_view = all_scores.pivot(index=['subject', 'model'], columns='split', values=['accuracy', 'balanced_accuracy'])\n",
    "display((100 * score_view).round(2))\n",
    "display(choices)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96029153",
   "metadata": {},
   "source": [
    "## Uncertainty in the paired model comparison\n",
    "\n",
    "Both models are evaluated on exactly the same test trials. We resample the six complete recording runs with replacement 2,000 times per participant and apply the same sampled runs to both models. The intervals are the 2.5th and 97.5th percentiles of the resulting distributions. Seeds are 1108 plus the participant number.\n",
    "\n",
    "This preserves dependence within a run and assumes the six runs are exchangeable for resampling. With only six runs, the intervals are exploratory and can be unstable. They describe variability among observed runs conditional on the fitted models and one recorded test session. They do not estimate uncertainty for new recording days, new people, or retraining. The displayed tables store proportions. The plot expresses paired differences in percentage points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2eba03a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>model</th>\n",
       "      <th>accuracy_low</th>\n",
       "      <th>accuracy_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>LDA</td>\n",
       "      <td>0.5371</td>\n",
       "      <td>0.6093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>0.5338</td>\n",
       "      <td>0.6536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>LDA</td>\n",
       "      <td>0.2686</td>\n",
       "      <td>0.3965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>0.2898</td>\n",
       "      <td>0.3930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>LDA</td>\n",
       "      <td>0.5652</td>\n",
       "      <td>0.6619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>0.5532</td>\n",
       "      <td>0.6679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>LDA</td>\n",
       "      <td>0.4248</td>\n",
       "      <td>0.5214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>0.4444</td>\n",
       "      <td>0.5148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject                model  accuracy_low  accuracy_high\n",
       "0        1                  LDA        0.5371         0.6093\n",
       "1        1  Logistic regression        0.5338         0.6536\n",
       "2        2                  LDA        0.2686         0.3965\n",
       "3        2  Logistic regression        0.2898         0.3930\n",
       "4        3                  LDA        0.5652         0.6619\n",
       "5        3  Logistic regression        0.5532         0.6679\n",
       "6        4                  LDA        0.4248         0.5214\n",
       "7        4  Logistic regression        0.4444         0.5148"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>day2_BA_difference_LR_minus_LDA</th>\n",
       "      <th>difference_low</th>\n",
       "      <th>difference_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0247</td>\n",
       "      <td>-0.0276</td>\n",
       "      <td>0.0774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0111</td>\n",
       "      <td>-0.0106</td>\n",
       "      <td>0.0358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.0078</td>\n",
       "      <td>-0.0270</td>\n",
       "      <td>0.0107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0098</td>\n",
       "      <td>-0.0245</td>\n",
       "      <td>0.0441</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject  day2_BA_difference_LR_minus_LDA  difference_low  difference_high\n",
       "0        1                           0.0247         -0.0276           0.0774\n",
       "1        2                           0.0111         -0.0106           0.0358\n",
       "2        3                          -0.0078         -0.0270           0.0107\n",
       "3        4                           0.0098         -0.0245           0.0441"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interval_tables = []\n",
    "difference_intervals = []\n",
    "for subject, result in participant_results.items():\n",
    "    interval, difference = bootstrap_paired_runs(result[3], seed=1108 + subject)\n",
    "    interval.insert(0, 'subject', subject)\n",
    "    interval_tables.append(interval)\n",
    "    difference_intervals.append({'subject': subject, **difference})\n",
    "accuracy_intervals = pd.concat(interval_tables, ignore_index=True)\n",
    "paired_intervals = choices[['subject', 'day2_BA_difference_LR_minus_LDA']].merge(pd.DataFrame(difference_intervals), on='subject')\n",
    "display(accuracy_intervals.round(4))\n",
    "display(paired_intervals.round(4))\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(11, 4.5), layout='constrained')\n",
    "colors = {'LDA': '#237b8d', 'Logistic regression': '#bc6227'}\n",
    "for name in models:\n",
    "    subset = all_scores.query(\"split == 'test' and model == @name\").sort_values('subject')\n",
    "    axes[0].plot(subset['subject'], 100 * subset['balanced_accuracy'], 'o-', label=name, color=colors[name])\n",
    "axes[0].axhline(25, linestyle='--', color='gray', linewidth=1)\n",
    "axes[0].set(xticks=[1,2,3,4], xlabel='Participant', ylabel='Balanced accuracy (%)', ylim=(0,100), title='Second-day performance')\n",
    "axes[0].legend(frameon=False)\n",
    "for _, row in paired_intervals.iterrows():\n",
    "    subject = row['subject']\n",
    "    axes[1].hlines(subject, 100 * row['difference_low'], 100 * row['difference_high'], color='#344354', linewidth=2)\n",
    "    axes[1].plot(100 * row['day2_BA_difference_LR_minus_LDA'], subject, 'o', color='#bc6227')\n",
    "axes[1].axvline(0, color='gray', linestyle='--')\n",
    "axes[1].set(yticks=[1,2,3,4], ylabel='Participant', xlabel='Logistic regression minus LDA (percentage points)', title='Paired differences with run bootstrap intervals')\n",
    "fig.suptitle('Participant 1 is illustrative · participants 2–4 are development data', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49a59dd1",
   "metadata": {},
   "source": [
    "## Recurring classification errors\n",
    "\n",
    "The next table reports the two most frequent directional errors for each participant and model on day two. The counts depend on how many trials of each true class were retained. Row percentages express each error as a proportion of the corresponding true class. The matrix panels show all classes, using a common color scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8b3782a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>model</th>\n",
       "      <th>actual</th>\n",
       "      <th>predicted</th>\n",
       "      <th>count</th>\n",
       "      <th>percent_of_true_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>LDA</td>\n",
       "      <td>left hand</td>\n",
       "      <td>right hand</td>\n",
       "      <td>29</td>\n",
       "      <td>40.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>LDA</td>\n",
       "      <td>feet</td>\n",
       "      <td>tongue</td>\n",
       "      <td>24</td>\n",
       "      <td>34.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>feet</td>\n",
       "      <td>tongue</td>\n",
       "      <td>23</td>\n",
       "      <td>33.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>right hand</td>\n",
       "      <td>left hand</td>\n",
       "      <td>23</td>\n",
       "      <td>32.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>LDA</td>\n",
       "      <td>tongue</td>\n",
       "      <td>feet</td>\n",
       "      <td>37</td>\n",
       "      <td>51.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>LDA</td>\n",
       "      <td>left hand</td>\n",
       "      <td>feet</td>\n",
       "      <td>36</td>\n",
       "      <td>50.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>tongue</td>\n",
       "      <td>feet</td>\n",
       "      <td>41</td>\n",
       "      <td>56.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>left hand</td>\n",
       "      <td>feet</td>\n",
       "      <td>40</td>\n",
       "      <td>56.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>LDA</td>\n",
       "      <td>feet</td>\n",
       "      <td>tongue</td>\n",
       "      <td>31</td>\n",
       "      <td>45.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>LDA</td>\n",
       "      <td>left hand</td>\n",
       "      <td>right hand</td>\n",
       "      <td>15</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>feet</td>\n",
       "      <td>tongue</td>\n",
       "      <td>26</td>\n",
       "      <td>38.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>left hand</td>\n",
       "      <td>right hand</td>\n",
       "      <td>20</td>\n",
       "      <td>29.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4</td>\n",
       "      <td>LDA</td>\n",
       "      <td>feet</td>\n",
       "      <td>tongue</td>\n",
       "      <td>23</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4</td>\n",
       "      <td>LDA</td>\n",
       "      <td>right hand</td>\n",
       "      <td>left hand</td>\n",
       "      <td>19</td>\n",
       "      <td>33.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4</td>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>feet</td>\n",
       "      <td>tongue</td>\n",
       "      <td>21</td>\n",
       "      <td>35.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4</td>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>left hand</td>\n",
       "      <td>right hand</td>\n",
       "      <td>21</td>\n",
       "      <td>35.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    subject                model      actual   predicted  count  \\\n",
       "0         1                  LDA   left hand  right hand     29   \n",
       "1         1                  LDA        feet      tongue     24   \n",
       "2         1  Logistic regression        feet      tongue     23   \n",
       "3         1  Logistic regression  right hand   left hand     23   \n",
       "4         2                  LDA      tongue        feet     37   \n",
       "5         2                  LDA   left hand        feet     36   \n",
       "6         2  Logistic regression      tongue        feet     41   \n",
       "7         2  Logistic regression   left hand        feet     40   \n",
       "8         3                  LDA        feet      tongue     31   \n",
       "9         3                  LDA   left hand  right hand     15   \n",
       "10        3  Logistic regression        feet      tongue     26   \n",
       "11        3  Logistic regression   left hand  right hand     20   \n",
       "12        4                  LDA        feet      tongue     23   \n",
       "13        4                  LDA  right hand   left hand     19   \n",
       "14        4  Logistic regression        feet      tongue     21   \n",
       "15        4  Logistic regression   left hand  right hand     21   \n",
       "\n",
       "    percent_of_true_class  \n",
       "0                    40.8  \n",
       "1                    34.8  \n",
       "2                    33.3  \n",
       "3                    32.9  \n",
       "4                    51.4  \n",
       "5                    50.7  \n",
       "6                    56.9  \n",
       "7                    56.3  \n",
       "8                    45.6  \n",
       "9                    22.4  \n",
       "10                   38.2  \n",
       "11                   29.9  \n",
       "12                   39.0  \n",
       "13                   33.3  \n",
       "14                   35.6  \n",
       "15                   35.6  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>model</th>\n",
       "      <th>smoothing</th>\n",
       "      <th>weakest_pair</th>\n",
       "      <th>gamma_min</th>\n",
       "      <th>U_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>LDA</td>\n",
       "      <td>0.5</td>\n",
       "      <td>left hand / feet</td>\n",
       "      <td>0.0973</td>\n",
       "      <td>14.8239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>LDA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>left hand / feet</td>\n",
       "      <td>0.0751</td>\n",
       "      <td>20.3510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>LDA</td>\n",
       "      <td>2.0</td>\n",
       "      <td>left hand / feet</td>\n",
       "      <td>0.0492</td>\n",
       "      <td>32.7076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>0.5</td>\n",
       "      <td>left hand / right hand</td>\n",
       "      <td>0.0847</td>\n",
       "      <td>16.0802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>1.0</td>\n",
       "      <td>left hand / right hand</td>\n",
       "      <td>0.0615</td>\n",
       "      <td>22.3119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>2.0</td>\n",
       "      <td>left hand / right hand</td>\n",
       "      <td>0.0385</td>\n",
       "      <td>36.1453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>LDA</td>\n",
       "      <td>0.5</td>\n",
       "      <td>left hand / right hand</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>LDA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>left hand / right hand</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>LDA</td>\n",
       "      <td>2.0</td>\n",
       "      <td>left hand / right hand</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>0.5</td>\n",
       "      <td>left hand / right hand</td>\n",
       "      <td>0.0116</td>\n",
       "      <td>100.7907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>1.0</td>\n",
       "      <td>left hand / right hand</td>\n",
       "      <td>0.0089</td>\n",
       "      <td>151.3740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>2.0</td>\n",
       "      <td>right hand / tongue</td>\n",
       "      <td>0.0051</td>\n",
       "      <td>272.4524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>LDA</td>\n",
       "      <td>0.5</td>\n",
       "      <td>left hand / feet</td>\n",
       "      <td>0.0528</td>\n",
       "      <td>18.6835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3</td>\n",
       "      <td>LDA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>left hand / feet</td>\n",
       "      <td>0.0397</td>\n",
       "      <td>25.4657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>LDA</td>\n",
       "      <td>2.0</td>\n",
       "      <td>left hand / feet</td>\n",
       "      <td>0.0260</td>\n",
       "      <td>40.0888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3</td>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>0.5</td>\n",
       "      <td>left hand / feet</td>\n",
       "      <td>0.0570</td>\n",
       "      <td>22.7754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3</td>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>1.0</td>\n",
       "      <td>left hand / feet</td>\n",
       "      <td>0.0438</td>\n",
       "      <td>30.6101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3</td>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>2.0</td>\n",
       "      <td>left hand / feet</td>\n",
       "      <td>0.0286</td>\n",
       "      <td>47.9108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>4</td>\n",
       "      <td>LDA</td>\n",
       "      <td>0.5</td>\n",
       "      <td>feet / tongue</td>\n",
       "      <td>0.0294</td>\n",
       "      <td>41.0510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4</td>\n",
       "      <td>LDA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>feet / tongue</td>\n",
       "      <td>0.0235</td>\n",
       "      <td>57.4428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4</td>\n",
       "      <td>LDA</td>\n",
       "      <td>2.0</td>\n",
       "      <td>feet / tongue</td>\n",
       "      <td>0.0163</td>\n",
       "      <td>95.1738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4</td>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>0.5</td>\n",
       "      <td>feet / tongue</td>\n",
       "      <td>0.0303</td>\n",
       "      <td>51.9090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4</td>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>1.0</td>\n",
       "      <td>feet / tongue</td>\n",
       "      <td>0.0243</td>\n",
       "      <td>71.1514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4</td>\n",
       "      <td>Logistic regression</td>\n",
       "      <td>2.0</td>\n",
       "      <td>left hand / right hand</td>\n",
       "      <td>0.0149</td>\n",
       "      <td>115.7129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    subject                model  smoothing            weakest_pair  \\\n",
       "0         1                  LDA        0.5        left hand / feet   \n",
       "1         1                  LDA        1.0        left hand / feet   \n",
       "2         1                  LDA        2.0        left hand / feet   \n",
       "3         1  Logistic regression        0.5  left hand / right hand   \n",
       "4         1  Logistic regression        1.0  left hand / right hand   \n",
       "5         1  Logistic regression        2.0  left hand / right hand   \n",
       "6         2                  LDA        0.5  left hand / right hand   \n",
       "7         2                  LDA        1.0  left hand / right hand   \n",
       "8         2                  LDA        2.0  left hand / right hand   \n",
       "9         2  Logistic regression        0.5  left hand / right hand   \n",
       "10        2  Logistic regression        1.0  left hand / right hand   \n",
       "11        2  Logistic regression        2.0     right hand / tongue   \n",
       "12        3                  LDA        0.5        left hand / feet   \n",
       "13        3                  LDA        1.0        left hand / feet   \n",
       "14        3                  LDA        2.0        left hand / feet   \n",
       "15        3  Logistic regression        0.5        left hand / feet   \n",
       "16        3  Logistic regression        1.0        left hand / feet   \n",
       "17        3  Logistic regression        2.0        left hand / feet   \n",
       "18        4                  LDA        0.5           feet / tongue   \n",
       "19        4                  LDA        1.0           feet / tongue   \n",
       "20        4                  LDA        2.0           feet / tongue   \n",
       "21        4  Logistic regression        0.5           feet / tongue   \n",
       "22        4  Logistic regression        1.0           feet / tongue   \n",
       "23        4  Logistic regression        2.0  left hand / right hand   \n",
       "\n",
       "    gamma_min      U_id  \n",
       "0      0.0973   14.8239  \n",
       "1      0.0751   20.3510  \n",
       "2      0.0492   32.7076  \n",
       "3      0.0847   16.0802  \n",
       "4      0.0615   22.3119  \n",
       "5      0.0385   36.1453  \n",
       "6      0.0000       inf  \n",
       "7      0.0000       inf  \n",
       "8      0.0000       inf  \n",
       "9      0.0116  100.7907  \n",
       "10     0.0089  151.3740  \n",
       "11     0.0051  272.4524  \n",
       "12     0.0528   18.6835  \n",
       "13     0.0397   25.4657  \n",
       "14     0.0260   40.0888  \n",
       "15     0.0570   22.7754  \n",
       "16     0.0438   30.6101  \n",
       "17     0.0286   47.9108  \n",
       "18     0.0294   41.0510  \n",
       "19     0.0235   57.4428  \n",
       "20     0.0163   95.1738  \n",
       "21     0.0303   51.9090  \n",
       "22     0.0243   71.1514  \n",
       "23     0.0149  115.7129  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "error_rows = []\n",
    "fig, axes = plt.subplots(4, 2, figsize=(10, 14), layout='constrained')\n",
    "for row_index, subject in enumerate([1,2,3,4]):\n",
    "    for column, name in enumerate(models):\n",
    "        record = all_scores.query(\"subject == @subject and model == @name and split == 'test'\").iloc[0]\n",
    "        counts = np.asarray(record['confusion'])\n",
    "        rates = counts / counts.sum(axis=1, keepdims=True)\n",
    "        off_diagonal = counts.copy()\n",
    "        np.fill_diagonal(off_diagonal, -1)\n",
    "        top_pairs = np.argsort(off_diagonal.ravel(), kind='stable')[-2:][::-1]\n",
    "        for flat_index in top_pairs:\n",
    "            actual, predicted = np.unravel_index(flat_index, counts.shape)\n",
    "            error_rows.append({'subject': subject, 'model': name,\n",
    "                               'actual': CLASS_NAMES[actual], 'predicted': CLASS_NAMES[predicted],\n",
    "                               'count': int(counts[actual,predicted]),\n",
    "                               'percent_of_true_class': 100 * rates[actual,predicted]})\n",
    "        axis = axes[row_index, column]\n",
    "        axis.imshow(rates, cmap='Blues', vmin=0, vmax=1)\n",
    "        for actual in range(4):\n",
    "            for predicted in range(4):\n",
    "                axis.text(predicted, actual, str(counts[actual,predicted]), ha='center', va='center',\n",
    "                          color='white' if rates[actual,predicted] > 0.55 else '#182838')\n",
    "        axis.set(xticks=range(4), yticks=range(4), xticklabels=CLASS_NAMES, yticklabels=CLASS_NAMES,\n",
    "                 xlabel='Predicted class', ylabel='Actual class', title=f'Participant {subject} · {name}')\n",
    "        axis.tick_params(axis='x',labelrotation=25)\n",
    "plt.show()\n",
    "recurring_errors = pd.DataFrame(error_rows)\n",
    "display(recurring_errors.round(1))\n",
    "display(all_diagnostics.round(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95a0665a",
   "metadata": {},
   "source": [
    "## Interpreting this extension\n",
    "\n",
    "**Observed results.** Day-two balanced accuracy is about 33–34% for participant 2, 60–61% for participant 3, and 48–49% for participant 4. The gap between the two classifiers is much smaller than this variation between people. Every paired run-bootstrap interval includes zero, so the observed ordering does not establish a reliable winner. These intervals describe resampling the observed runs, not future-day or population uncertainty.\n",
    "\n",
    "Validation balanced accuracy selects LDA for all three development participants. The primary U_id diagnostic selects logistic regression for participant 2 and LDA for participants 3 and 4. The observed day-two balanced-accuracy ordering agrees in 1 of 3 cases for the accuracy rule and 2 of 3 for U_id. Three small, uncertain comparisons cannot establish superior model selection. These counts are descriptive. The original participant-1 demonstration is kept separate.\n",
    "\n",
    "**Why one value is infinite.** Participant 2's LDA validation confusion matrix has identical first and second rows, both [3, 3, 0, 6]. After smoothing and normalization, the estimated left-hand and right-hand output distributions remain identical. Their estimated Chernoff information is zero, so U_id has no finite value under this estimated channel. This does not show that the true population distributions are identical or that control is impossible. There are only 12 validation trials in each of these classes.\n",
    "\n",
    "Increasing smoothing from 0.5 to 2 changes diagnostic magnitudes substantially, although the preferred classifier is unchanged for each participant. Participant 4 excludes 60 of 288 test trials (20.8%) because of source artifact flags. The reported performance is conditional on accepted trials and excludes those difficult recordings.\n",
    "\n",
    "**Next experiment.** Keep model exploration within participants 1–4. Specify an endpoint and freeze every feature, model, selection rule, exclusion rule, and analysis setting before a single evaluation on reserved participants 5–9. If the target is next-day balanced accuracy, measure that directly. Testing the relationship between U_id and sequential-controller cost requires an explicitly defined controller, observation process, and cost, which are not implemented here."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6d14d66",
   "metadata": {},
   "source": [
    "## Sources and provenance\n",
    "\n",
    "- Kim et al. (2026), *Beyond Accuracy*. User-supplied arXiv v1, especially pp. 3–5, 7–9, and Appendix G. [Paper](https://arxiv.org/abs/2611.05658).\n",
    "- Zayegh and Refai (2026), *Implementation and Evaluation of a Deep Learning-Based Wireless Decoder Using Real-World Over-the-Air Data*. User-supplied IEEE Access article. [DOI](https://doi.org/10.1109/ACCESS.2026.3652491).\n",
    "- BNCI Horizon 2020 dataset 001-2014, Graz University of Technology. A01T/E.mat through A04T/E.mat, participants 1–4. Dataset page identifies CC BY-ND 4.0. Raw files are retrieved directly, and source SHA-256 digests are recorded in this notebook. [Dataset and license](https://bnci-horizon-2020.eu/database/data-sets), [task description](https://bnci-horizon-2020.eu/database/data-sets/001-2014/description.pdf).\n",
    "- Tangermann et al. (2012), *Review of the BCI Competition IV*. [DOI](https://doi.org/10.3389/fnins.2012.00055).\n",
    "- MOABB's BNCI loader documents the corresponding trial conversion. [Loader](https://github.com/NeuroTechX/moabb/blob/develop/moabb/datasets/bnci/base.py).\n",
    "\n",
    "The output tables and plots are from the independent implementation in this notebook. Artifact exclusions use supplied annotations. These offline development results do not establish population generalization, real-time control performance, or customer demand."
   ]
  }
 ],
 "metadata": {
  "experiment_status": "Exploratory development on participants 1–4; participants 5–9 reserved.",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.12.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}