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   "source": [
    "# Reproducing: Mwata-Velu et al. (2023)\n",
    "\n",
    "**Paper**: \"EEG-BCI Features Discrimination between Executed and Imagined Movements Based on FastICA, Hjorth Parameters, and SVM\"  \n",
    "**Journal**: Mathematics 2023, 11, 4409  \n",
    "**DOI**: https://doi.org/10.3390/math11214409\n",
    "\n",
    "## ICA Strategy\n",
    "\n",
    "The paper does not specify whether ICA is fitted per-run, per-subject, or globally.\n",
    "This notebook supports all three strategies via `config.ICA_STRATEGY`.\n",
    "Change it in `config.py` before running.\n",
    "\n",
    "## Key Implementation Decisions\n",
    "\n",
    "| Decision | Paper says | We do | Rationale |\n",
    "|----------|-----------|-------|----------|\n",
    "| Pipeline order | Figure 1: filter→ICA; Algorithm 1: ICA with internal sub-band eval | ICA then sub-band eval | Energy criterion is meaningless on pre-filtered data |\n",
    "| ICA algorithm | Gram-Schmidt (Algorithm 1, Step 3) | `algorithm='deflation'` | Deflation uses Gram-Schmidt |\n",
    "| Energy criterion | ∀χ ∈ {α, β, **γ**} | ∀χ ∈ {**θ**, α, β} | γ never defined; likely typo for θ |\n",
    "| ICA scope | Not specified | Configurable | Reproducibility variable |\n",
    "| Classification Method | Methods 1 and 2 | Method 2 only (cross-subject) | Method 1 split is contradictory |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import mne\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore', category=RuntimeWarning)  # MNE verbosity\n",
    "mne.set_log_level('WARNING')\n",
    "\n",
    "from config import (\n",
    "    ICA_STRATEGY, DATA_DIR, TARGET_CHANNELS,\n",
    "    TRAIN_SUBJECTS, TEST_SUBJECTS, VAL_SUBJECTS, TARGET_RUNS,\n",
    ")\n",
    "from pipeline import (\n",
    "    process_subject_per_run, process_subject_per_subject,\n",
    "    process_subject_global, fit_global_ica,\n",
    "    classify_with_averaging, print_results_summary,\n",
    ")\n",
    "\n",
    "print(f\"Data directory: {DATA_DIR}\")\n",
    "print(f\"ICA strategy: {ICA_STRATEGY}\")\n",
    "print(f\"Target channels: {TARGET_CHANNELS}\")\n",
    "print(f\"Subjects: {len(TRAIN_SUBJECTS)} train, {len(TEST_SUBJECTS)} test, {len(VAL_SUBJECTS)} val\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Process All Subjects (Method 2: Cross-Subject Split)\n",
    "\n",
    "Table 4 from the paper:\n",
    "- Training: Subjects 1-83 (80%)\n",
    "- Testing: Subjects 84-93 (10%)\n",
    "- Validation: Subjects 94-103 (10%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If using global ICA, fit it first on training subjects\n",
    "global_ica_results = None\n",
    "if ICA_STRATEGY == 'global':\n",
    "    print(\"Fitting global ICA on all training subjects...\")\n",
    "    global_ica_results = fit_global_ica(TRAIN_SUBJECTS)\n",
    "    print(\"Global ICA fitting complete.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_subject_group(subject_list, group_name):\n",
    "    \"\"\"Process a group of subjects and collect features/labels.\"\"\"\n",
    "    all_features = []\n",
    "    all_labels = []\n",
    "    total_kept = 0\n",
    "    n_processed = 0\n",
    "    \n",
    "    print(f\"\\n{'='*60}\")\n",
    "    print(f\"PROCESSING {group_name} ({len(subject_list)} subjects)\")\n",
    "    print(f\"{'='*60}\")\n",
    "    \n",
    "    for subject_num in subject_list:\n",
    "        try:\n",
    "            if ICA_STRATEGY == 'global':\n",
    "                feats, labels, runs, n_kept = process_subject_global(\n",
    "                    subject_num, global_ica_results, verbose=True)\n",
    "            elif ICA_STRATEGY == 'per_subject':\n",
    "                feats, labels, runs, n_kept = process_subject_per_subject(\n",
    "                    subject_num, verbose=True)\n",
    "            elif ICA_STRATEGY == 'per_run':\n",
    "                feats, labels, runs, n_kept = process_subject_per_run(\n",
    "                    subject_num, verbose=True)\n",
    "            \n",
    "            if len(feats) > 0:\n",
    "                combined_feats = np.vstack(feats)\n",
    "                combined_labels = np.concatenate(labels)\n",
    "                all_features.append(combined_feats)\n",
    "                all_labels.append(combined_labels)\n",
    "                total_kept += n_kept\n",
    "                n_processed += 1\n",
    "                \n",
    "                n_me = np.sum(combined_labels == 'ME')\n",
    "                n_mi = np.sum(combined_labels == 'MI')\n",
    "                print(f\"  S{subject_num:03d}: {len(combined_labels)} epochs \"\n",
    "                      f\"(ME={n_me}, MI={n_mi}), {n_kept} ICA components kept\")\n",
    "        except Exception as e:\n",
    "            print(f\"  S{subject_num:03d}: ERROR - {e}\")\n",
    "            continue\n",
    "    \n",
    "    if len(all_features) > 0:\n",
    "        X = np.vstack(all_features)\n",
    "        y = np.concatenate(all_labels)\n",
    "    else:\n",
    "        X = np.array([])\n",
    "        y = np.array([])\n",
    "    \n",
    "    print(f\"\\n  {group_name} total: {len(y)} samples \"\n",
    "          f\"(ME={np.sum(y=='ME')}, MI={np.sum(y=='MI')})\")\n",
    "    if n_processed > 0:\n",
    "        print(f\"  Avg ICA components kept: {total_kept / n_processed:.1f}\")\n",
    "    \n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = process_subject_group(TRAIN_SUBJECTS, \"TRAINING\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test, y_test = process_subject_group(TEST_SUBJECTS, \"TESTING\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val, y_val = process_subject_group(VAL_SUBJECTS, \"VALIDATION\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Summary\n",
    "print(f\"\\n{'='*60}\")\n",
    "print(f\"DATASET SUMMARY\")\n",
    "print(f\"{'='*60}\")\n",
    "print(f\"  Training:   {X_train.shape} — ME={np.sum(y_train=='ME')}, MI={np.sum(y_train=='MI')}\")\n",
    "print(f\"  Testing:    {X_test.shape}  — ME={np.sum(y_test=='ME')}, MI={np.sum(y_test=='MI')}\")\n",
    "print(f\"  Validation: {X_val.shape}  — ME={np.sum(y_val=='ME')}, MI={np.sum(y_val=='MI')}\")\n",
    "print(f\"\\n  Paper expects: ~6972 train, ~840 test, ~840 val\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Classification with 5-Run Averaging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = classify_with_averaging(\n",
    "    X_train, y_train,\n",
    "    X_test, y_test,\n",
    "    X_val, y_val,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print_results_summary(results)"
   ]
  }
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