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reproduction_notebook.ipynb
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245
reproduction_notebook.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Reproducing: Mwata-Velu et al. (2023)\n",
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"\n",
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"**Paper**: \"EEG-BCI Features Discrimination between Executed and Imagined Movements Based on FastICA, Hjorth Parameters, and SVM\" \n",
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"**Journal**: Mathematics 2023, 11, 4409 \n",
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"**DOI**: https://doi.org/10.3390/math11214409\n",
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"\n",
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"## ICA Strategy\n",
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"\n",
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"The paper does not specify whether ICA is fitted per-run, per-subject, or globally.\n",
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"This notebook supports all three strategies via `config.ICA_STRATEGY`.\n",
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"Change it in `config.py` before running.\n",
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"\n",
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"## Key Implementation Decisions\n",
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"\n",
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"| Decision | Paper says | We do | Rationale |\n",
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"|----------|-----------|-------|----------|\n",
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"| 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",
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"| ICA algorithm | Gram-Schmidt (Algorithm 1, Step 3) | `algorithm='deflation'` | Deflation uses Gram-Schmidt |\n",
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"| Energy criterion | ∀χ ∈ {α, β, **γ**} | ∀χ ∈ {**θ**, α, β} | γ never defined; likely typo for θ |\n",
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"| ICA scope | Not specified | Configurable | Reproducibility variable |\n",
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"| Classification Method | Methods 1 and 2 | Method 2 only (cross-subject) | Method 1 split is contradictory |"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1. Setup"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import mne\n",
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"import warnings\n",
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"warnings.filterwarnings('ignore', category=RuntimeWarning) # MNE verbosity\n",
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"mne.set_log_level('WARNING')\n",
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"\n",
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"from config import (\n",
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" ICA_STRATEGY, DATA_DIR, TARGET_CHANNELS,\n",
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" TRAIN_SUBJECTS, TEST_SUBJECTS, VAL_SUBJECTS, TARGET_RUNS,\n",
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")\n",
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"from pipeline import (\n",
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" process_subject_per_run, process_subject_per_subject,\n",
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" process_subject_global, fit_global_ica,\n",
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" classify_with_averaging, print_results_summary,\n",
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")\n",
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"\n",
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"print(f\"Data directory: {DATA_DIR}\")\n",
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"print(f\"ICA strategy: {ICA_STRATEGY}\")\n",
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"print(f\"Target channels: {TARGET_CHANNELS}\")\n",
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"print(f\"Subjects: {len(TRAIN_SUBJECTS)} train, {len(TEST_SUBJECTS)} test, {len(VAL_SUBJECTS)} val\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 2. Process All Subjects (Method 2: Cross-Subject Split)\n",
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"\n",
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"Table 4 from the paper:\n",
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"- Training: Subjects 1-83 (80%)\n",
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"- Testing: Subjects 84-93 (10%)\n",
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"- Validation: Subjects 94-103 (10%)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# If using global ICA, fit it first on training subjects\n",
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"global_ica_results = None\n",
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"if ICA_STRATEGY == 'global':\n",
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" print(\"Fitting global ICA on all training subjects...\")\n",
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" global_ica_results = fit_global_ica(TRAIN_SUBJECTS)\n",
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" print(\"Global ICA fitting complete.\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def process_subject_group(subject_list, group_name):\n",
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" \"\"\"Process a group of subjects and collect features/labels.\"\"\"\n",
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" all_features = []\n",
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" all_labels = []\n",
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" total_kept = 0\n",
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" n_processed = 0\n",
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" \n",
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" print(f\"\\n{'='*60}\")\n",
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" print(f\"PROCESSING {group_name} ({len(subject_list)} subjects)\")\n",
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" print(f\"{'='*60}\")\n",
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" \n",
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" for subject_num in subject_list:\n",
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" try:\n",
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" if ICA_STRATEGY == 'global':\n",
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" feats, labels, runs, n_kept = process_subject_global(\n",
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" subject_num, global_ica_results, verbose=True)\n",
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" elif ICA_STRATEGY == 'per_subject':\n",
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" feats, labels, runs, n_kept = process_subject_per_subject(\n",
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" subject_num, verbose=True)\n",
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" elif ICA_STRATEGY == 'per_run':\n",
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" feats, labels, runs, n_kept = process_subject_per_run(\n",
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" subject_num, verbose=True)\n",
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" \n",
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" if len(feats) > 0:\n",
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" combined_feats = np.vstack(feats)\n",
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" combined_labels = np.concatenate(labels)\n",
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" all_features.append(combined_feats)\n",
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" all_labels.append(combined_labels)\n",
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" total_kept += n_kept\n",
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" n_processed += 1\n",
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" \n",
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" n_me = np.sum(combined_labels == 'ME')\n",
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" n_mi = np.sum(combined_labels == 'MI')\n",
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" print(f\" S{subject_num:03d}: {len(combined_labels)} epochs \"\n",
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" f\"(ME={n_me}, MI={n_mi}), {n_kept} ICA components kept\")\n",
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" except Exception as e:\n",
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" print(f\" S{subject_num:03d}: ERROR - {e}\")\n",
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" continue\n",
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" \n",
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" if len(all_features) > 0:\n",
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" X = np.vstack(all_features)\n",
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" y = np.concatenate(all_labels)\n",
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" else:\n",
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" X = np.array([])\n",
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" y = np.array([])\n",
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" \n",
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" print(f\"\\n {group_name} total: {len(y)} samples \"\n",
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" f\"(ME={np.sum(y=='ME')}, MI={np.sum(y=='MI')})\")\n",
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" if n_processed > 0:\n",
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" print(f\" Avg ICA components kept: {total_kept / n_processed:.1f}\")\n",
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" \n",
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" return X, y"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"X_train, y_train = process_subject_group(TRAIN_SUBJECTS, \"TRAINING\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"X_test, y_test = process_subject_group(TEST_SUBJECTS, \"TESTING\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"X_val, y_val = process_subject_group(VAL_SUBJECTS, \"VALIDATION\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Summary\n",
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"print(f\"\\n{'='*60}\")\n",
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"print(f\"DATASET SUMMARY\")\n",
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"print(f\"{'='*60}\")\n",
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"print(f\" Training: {X_train.shape} — ME={np.sum(y_train=='ME')}, MI={np.sum(y_train=='MI')}\")\n",
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"print(f\" Testing: {X_test.shape} — ME={np.sum(y_test=='ME')}, MI={np.sum(y_test=='MI')}\")\n",
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"print(f\" Validation: {X_val.shape} — ME={np.sum(y_val=='ME')}, MI={np.sum(y_val=='MI')}\")\n",
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"print(f\"\\n Paper expects: ~6972 train, ~840 test, ~840 val\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 4. Classification with 5-Run Averaging"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"results = classify_with_averaging(\n",
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" X_train, y_train,\n",
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" X_test, y_test,\n",
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" X_val, y_val,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print_results_summary(results)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "THESIS",
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"language": "python",
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"name": "thesis"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.23"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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