{ "cells": [ { "cell_type": "markdown", "id": "91d65ed4", "metadata": {}, "source": [ "# Study 002 · How much calibration helps on the next recording day?\n", "\n", "Daybreak Neural research project · 8 November 2026\n", "\n", "**Exploratory development study, not peer reviewed.** Participants 1–4 only. Participants 5–9 remain reserved.\n", "\n", "**Completed result.** Adding alignment did not demonstrate an advantage over the same supervised update at the preselected 24-original-trial budget. The four-person mean was **66.73%** for the combined method and **67.47%** for update-only. The primary difference was **-0.74 percentage points**, with a **95% conditional paired run-bootstrap interval [-2.29, +0.66]**. All four participant-level primary intervals included zero.\n", "\n", "The study used **893 accepted test trials**, the same five later runs per person at every budget. The 24-trial opportunity yielded **22, 24, 22 and 20 accepted calibration trials**. Participants 5–9 were not loaded or evaluated.\n", "\n", "The short-prefix result is a development warning: for participant 3, alignment-only at eight original trials fell from **80.43% to 49.41%**; the combined method scored **52.00%**, while update-only scored **79.53%**. This is an observed failure case, not a population estimate or proof of its cause." ] }, { "cell_type": "markdown", "id": "ebc49167", "metadata": {}, "source": [ "## Context and methods\n", "\n", "The primary question is whether covariance alignment adds value **beyond the same supervised update** at a budget of 24 original trials. Four related methods share a source-trained representation: frozen, alignment only, supervised update only, and alignment plus update. Frozen bandpower–LDA, CSP–LDA, and raw tangent-space–LR are additional reference pipelines.\n", "\n", "All accepted day-one task trials train a separate model for each person. The first day-two task run supplies the chronological calibration prefixes 0, 8, 16, 24, and 48. Artifact exclusions happen after those boundaries. The same five later runs supply the accepted test trials for every method and budget. Nothing from those later trials enters a fitted normalization, spatial filter, tangent reference, calibration transform, or classifier.\n", "\n", "**Key assumptions and limits.** Source artifact flags define an offline accepted-trial analysis. Each prediction uses a full four-second imagery epoch. Alignment with zero labels still consumes calibration EEG. The protocol was fixed locally before this computation, after Study001 had already examined these participants; it is not externally preregistered. Day-one training and day-two test sets differ from Study001, so scores across the two studies are not an adaptation comparison. No result demonstrates live control, future-day generalization, clinical benefit or a new algorithm." ] }, { "cell_type": "markdown", "id": "4b98868c", "metadata": {}, "source": [ "**Execution record.** All code cells were executed top-to-bottom in a fresh Python namespace, with tables and figures saved. This environment blocks Jupyter kernel sockets, so execution used an in-process Python runner. The interactive Jupyter interface was not tested here." ] }, { "cell_type": "markdown", "id": "104e11ca", "metadata": {}, "source": [ "### Setup\n", "\n", "Select **Python 3 (ipykernel)**. A first rerun may download eight public MATLAB source files; cached files are checked against recorded SHA-256 digests. Only participants 1–4 are allowed by the loader. Do not modify this restriction while developing the method.\n", "\n", "The accompanying bundle includes pinned `requirements.txt`. Install missing dependencies with `%pip install numpy scipy pandas scikit-learn mne pyriemann matplotlib threadpoolctl` and restart the kernel if needed. Exact versions used for this execution are recorded in the outputs.\n", "\n", "The `NEURAL_DATA_DIR` environment variable optionally selects an existing data cache. Otherwise the notebook uses `eeg_data` under its working directory. No private account, hosted service, or API key is needed." ] }, { "cell_type": "code", "execution_count": 1, "id": "015a5c00", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data directory: /workspace/scratch/b15a265229b0/tmp/eeg_data\n", "Result directory: /workspace/scratch/b15a265229b0/calibration_study/results\n" ] } ], "source": [ "from pathlib import Path\n", "import os, json, hashlib\n", "from IPython.display import display\n", "from threadpoolctl import threadpool_limits\n", "DATA_DIRECTORY = Path(os.environ.get('NEURAL_DATA_DIR', 'eeg_data')).expanduser().resolve()\n", "OUTPUT_DIRECTORY = Path('results').resolve()\n", "OUTPUT_DIRECTORY.mkdir(parents=True, exist_ok=True)\n", "print('Data directory:', DATA_DIRECTORY)\n", "print('Result directory:', OUTPUT_DIRECTORY)" ] }, { "cell_type": "markdown", "id": "c66f9a79", "metadata": {}, "source": [ "### Fixed protocol record\n", "\n", "This is the locally timestamped design fixed before execution. The supervised update retains source data and assigns target mixture fraction `n/(n+48)`, where `n` is accepted calibration count. Total training weight remains equal to the source sample count at every budget. This fixed weight is a design choice, not a validated optimum." ] }, { "cell_type": "code", "execution_count": 2, "id": "255d6e1d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"study\": \"002\",\n", " \"frozen_at_utc\": \"2026-11-08T19:54:33.940268+00:00\",\n", " \"scope\": \"exploratory development, not externally preregistered\",\n", " \"subjects\": [\n", " 1,\n", " 2,\n", " 3,\n", " 4\n", " ],\n", " \"reserved_subjects\": [\n", " 5,\n", " 6,\n", " 7,\n", " 8,\n", " 9\n", " ],\n", " \"budgets_original_trials\": [\n", " 0,\n", " 8,\n", " 16,\n", " 24,\n", " 48\n", " ],\n", " \"source_training\": \"all accepted day-one task trials\",\n", " \"calibration\": \"accepted subset of original chronological prefix of first day-two task run\",\n", " \"test\": \"all accepted trials in day-two task runs2\\u20136, fixed for all budgets/methods\",\n", " \"primary\": {\n", " \"budget\": 24,\n", " \"contrast\": \"combined minus update_only\",\n", " \"endpoint\": \"equal-participant mean balanced accuracy\",\n", " \"unit\": \"percentage points\"\n", " },\n", " \"bootstrap_replicates\": 5000,\n", " \"bootstrap_seed\": 20261108,\n", " \"target_mixture_weight\": \"n_accepted / (n_accepted + 48)\",\n", " \"total_sample_weight\": \"N_source\",\n", " \"filter\": \"Butterworth4thorder8\\u201330Hz causal sosfilt peroriginaltrial [0,6)s, crop [2,6)s; no inter-trial state\",\n", " \"covariance\": \"OAS pertrial, followed by covariance congruence for alignment variant\",\n", " \"tangent_space\": {\n", " \"metric\": \"riemann\",\n", " \"tsupdate\": false,\n", " \"reference\": \"source-only\"\n", " },\n", " \"methods\": [\n", " \"bandpower_LDA\",\n", " \"CSP_LDA\",\n", " \"raw_tangent_LR\",\n", " \"frozen\",\n", " \"alignment_only\",\n", " \"update_only\",\n", " \"combined\"\n", " ],\n", " \"adaptation_setting\": \"fixed at end of prefix; no later online updates\"\n", "}\n" ] } ], "source": [ "FROZEN_PROTOCOL = {'study': '002', 'frozen_at_utc': '2026-11-08T19:54:33.940268+00:00', 'scope': 'exploratory development, not externally preregistered', 'subjects': [1, 2, 3, 4], 'reserved_subjects': [5, 6, 7, 8, 9], 'budgets_original_trials': [0, 8, 16, 24, 48], 'source_training': 'all accepted day-one task trials', 'calibration': 'accepted subset of original chronological prefix of first day-two task run', 'test': 'all accepted trials in day-two task runs2–6, fixed for all budgets/methods', 'primary': {'budget': 24, 'contrast': 'combined minus update_only', 'endpoint': 'equal-participant mean balanced accuracy', 'unit': 'percentage points'}, 'bootstrap_replicates': 5000, 'bootstrap_seed': 20261108, 'target_mixture_weight': 'n_accepted / (n_accepted + 48)', 'total_sample_weight': 'N_source', 'filter': 'Butterworth4thorder8–30Hz causal sosfilt peroriginaltrial [0,6)s, crop [2,6)s; no inter-trial state', 'lr': {'C': 1.0, 'solver': 'lbfgs', 'max_iter': 3000, 'tol': 1e-06, 'class_weight': None}, 'csp': {'n_components': 8, 'reg': 'oas', 'cov_est': 'epoch', 'rank': 'full', 'log': True, 'norm_trace': False, 'component_order': 'mutual_info'}, 'covariance': 'OAS pertrial, followed by covariance congruence for alignment variant', 'tangent_space': {'metric': 'riemann', 'tsupdate': False, 'reference': 'source-only'}, 'methods': ['bandpower_LDA', 'CSP_LDA', 'raw_tangent_LR', 'frozen', 'alignment_only', 'update_only', 'combined'], 'adaptation_setting': 'fixed at end of prefix; no later online updates', 'source_hashes': {'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", "print(json.dumps({k:v for k,v in FROZEN_PROTOCOL.items() if k not in ['source_hashes','csp','lr']}, indent=2))" ] }, { "cell_type": "markdown", "id": "10d040ad", "metadata": {}, "source": [ "### Reproducible implementation\n", "\n", "The following collapsed definition cells contain the complete implementation; expand them to inspect individual functions. The spatial/covariance branches use causal 8–30 Hz filtering within each original six-second trial, retaining the four-second imagery interval. OAS covariance is computed before covariance recentering. Tangent references and standardizers are fitted on source data only, with prediction-batch reference updates disabled.\n", "\n", "Covariance recentering follows the Euclidean-alignment idea but is not an exact reproduction of raw-signal alignment with covariance shrinkage recomputed afterward." ] }, { "cell_type": "code", "execution_count": 3, "id": "a0bd97fe", "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "\"\"\"Study 002 fixed chronological calibration benchmark; participants 1--4 only.\n", "\n", "Existing-method ablation, not a novel-algorithm claim. Prediction occurs after\n", "the complete four-second epoch. No model/hyperparameter selection is performed.\n", "\"\"\"\n", "\n", "from __future__ import annotations\n", "\n", "import argparse\n", "\n", "import hashlib\n", "\n", "import json\n", "\n", "import platform\n", "\n", "import time\n", "\n", "import urllib.request\n", "\n", "import warnings\n", "\n", "from datetime import datetime, timezone\n", "\n", "from pathlib import Path\n", "\n", "import mne\n", "\n", "import numpy as np\n", "\n", "import pandas as pd\n", "\n", "import pyriemann\n", "\n", "import scipy\n", "\n", "import sklearn\n", "\n", "from mne.decoding import CSP\n", "\n", "from pyriemann.estimation import Covariances\n", "\n", "from pyriemann.tangentspace import TangentSpace\n", "\n", "from scipy.integrate import trapezoid\n", "\n", "from scipy.io import loadmat\n", "\n", "from scipy.signal import butter, sosfilt, welch\n", "\n", "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", "\n", "from sklearn.exceptions import ConvergenceWarning\n", "\n", "from sklearn.linear_model import LogisticRegression\n", "\n", "from sklearn.metrics import accuracy_score, balanced_accuracy_score\n", "\n", "from sklearn.preprocessing import StandardScaler\n", "\n", "SUBJECTS = (1, 2, 3, 4)\n", "\n", "BUDGETS = (0, 8, 16, 24, 48)\n", "\n", "LABELS = (1, 2, 3, 4)\n", "\n", "CLASS_NAMES = (\"left hand\", \"right hand\", \"feet\", \"tongue\")\n", "\n", "METHODS = (\"bandpower_LDA\", \"CSP_LDA\", \"raw_tangent_LR\", \"frozen\",\n", " \"alignment_only\", \"update_only\", \"combined\")\n", "\n", "ABLATION_METHODS = (\"frozen\", \"alignment_only\", \"update_only\", \"combined\")\n", "\n", "SOURCE_URL = \"https://bnci-horizon-2020.eu/database/data-sets/001-2014/\"\n", "\n", "EXPECTED_SHA256 = {\n", " \"A01E.mat\": \"53d415f39c3d7b0c88b894d7b08d99bcdfe855ede63831d3691af1a45607fb62\",\n", " \"A01T.mat\": \"054f02e70cf9c4ada1517e9b9864f45407939c1062c6793516585c6f511d0325\",\n", " \"A02E.mat\": \"d63c454005d3a9b41d8440629482e855afc823339bdd0b5721842a7ee9cc7b12\",\n", " \"A02T.mat\": \"5ddd5cb520b1692c3ba1363f48d98f58f0e46f3699ee50d749947950fc39db27\",\n", " \"A03E.mat\": \"d4229267ec7624fa8bd3af5cbebac17f415f7c722de6cb676748f8cb3b717d97\",\n", " \"A03T.mat\": \"7e731ee8b681d5da6ecb11ae1d4e64b1653c7f15aad5d6b7620b25ce53141e80\",\n", " \"A04E.mat\": \"81916dff2c12997974ba50ffc311da006ea66e525010d010765f0047e771c86a\",\n", " \"A04T.mat\": \"15850d81b95fc88cc8b9589eb9b713d49fa071e28adaf32d675b3eaa30591d6e\",\n", "}" ] }, { "cell_type": "code", "execution_count": 4, "id": "b8d23d60", "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "def _require(condition, message):\n", " if not bool(condition):\n", " raise AssertionError(message)" ] }, { "cell_type": "code", "execution_count": 5, "id": "58b30cdb", "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "def _sha256(path):\n", " digest = hashlib.sha256()\n", " with Path(path).open(\"rb\") as stream:\n", " for chunk in iter(lambda: stream.read(1024 * 1024), b\"\"):\n", " digest.update(chunk)\n", " return digest.hexdigest()" ] }, { "cell_type": "code", "execution_count": 6, "id": "c597e586", "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "def _verified_source_file(data_dir, filename):\n", " \"\"\"Use the cache or atomically download one of eight allowlisted files.\n", "\n", " Downloads are capped at 128 MiB and approximately four minutes including a\n", " final blocking read (60-second per-operation timeout, 180-second deadline).\n", " A file is never promoted from .part unless its recorded hash matches.\n", " \"\"\"\n", " if filename not in EXPECTED_SHA256:\n", " raise ValueError(\"Source file is outside the development-participant allowlist\")\n", " directory = Path(data_dir)\n", " directory.mkdir(parents=True, exist_ok=True)\n", " path = directory / filename\n", " if not path.exists():\n", " temporary = directory / (filename + \".part\")\n", " started, size = time.monotonic(), 0\n", " try:\n", " with urllib.request.urlopen(SOURCE_URL + filename, timeout=60) as response:\n", " with temporary.open(\"wb\") as stream:\n", " while True:\n", " if time.monotonic() - started > 180:\n", " raise TimeoutError(f\"Download deadline exceeded: {filename}\")\n", " chunk = response.read(1024 * 1024)\n", " if not chunk:\n", " break\n", " size += len(chunk)\n", " if size > 128 * 1024 * 1024:\n", " raise ValueError(f\"Download size limit exceeded: {filename}\")\n", " stream.write(chunk)\n", " _require(_sha256(temporary) == EXPECTED_SHA256[filename],\n", " f\"Downloaded source hash mismatch: {filename}\")\n", " temporary.replace(path)\n", " finally:\n", " if temporary.exists():\n", " temporary.unlink()\n", " _require(_sha256(path) == EXPECTED_SHA256[filename], f\"Source hash mismatch: {filename}\")\n", " return path" ] }, { "cell_type": "code", "execution_count": 7, "id": "a684845f", "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "def _legacy_bandpower(epoch):\n", " frequencies, psd = welch(epoch, fs=250, window=\"hann\", nperseg=250,\n", " noverlap=125, detrend=\"constant\", scaling=\"density\", axis=0)\n", " features = []\n", " for low, high in ((8.0, 12.0), (13.0, 30.0)):\n", " selected = (frequencies >= low) & (frequencies <= high)\n", " power = trapezoid(psd[selected], frequencies[selected], axis=0)\n", " _require(np.all(power > 0), \"Nonpositive bandpower\")\n", " features.extend(np.log10(power))\n", " return np.asarray(features, dtype=np.float64)" ] }, { "cell_type": "code", "execution_count": 8, "id": "f3c7f65c", "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "def load_participant(subject, data_dir):\n", " \"\"\"Verified files, all-original-trial ledger, and row-aligned feature arrays.\n", "\n", " Causal filtering has zero initial state separately for every original\n", " [0,6) s trial. Crop to [2,6) s, then independently demean each channel.\n", " \"\"\"\n", " if isinstance(subject, bool) or subject not in SUBJECTS:\n", " raise ValueError(\"Only development participants 1, 2, 3, 4 are allowed\")\n", " subject = int(subject)\n", " sos = butter(4, [8.0, 30.0], btype=\"bandpass\", fs=250, output=\"sos\")\n", " rows, epochs, bandpower, files = [], [], [], []\n", " for session, suffix in ((1, \"T\"), (2, \"E\")):\n", " filename = f\"A{subject:02d}{suffix}.mat\"\n", " path = _verified_source_file(data_dir, filename)\n", " digest = _sha256(path)\n", " _require(digest == EXPECTED_SHA256[filename], f\"Source hash mismatch: {filename}\")\n", " files.append({\"filename\": filename, \"sha256\": digest,\n", " \"source_url\": SOURCE_URL + filename, \"bytes\": path.stat().st_size})\n", " runs = np.atleast_1d(loadmat(path, simplify_cells=True)[\"data\"])\n", " task_run, session_trial = 0, 0\n", " for source_run, run in enumerate(runs, 1):\n", " starts = np.asarray(run[\"trial\"], dtype=int).reshape(-1)\n", " if len(starts) == 0:\n", " continue\n", " task_run += 1\n", " labels = np.asarray(run[\"y\"], dtype=int).reshape(-1)\n", " flags = np.asarray(run[\"artifacts\"], dtype=int).reshape(-1)\n", " signal = np.asarray(run[\"X\"], dtype=np.float64)\n", " _require(int(run[\"fs\"]) == 250 and signal.shape[1] == 25, \"Unexpected EEG layout\")\n", " _require(len(starts) == len(labels) == len(flags) == 48, \"Expected 48 trials/run\")\n", " _require(tuple(run[\"classes\"]) == CLASS_NAMES, \"Unexpected class order\")\n", " _require(set(flags).issubset({0, 1}), \"Unexpected artifact flag\")\n", " for within_run, (start, label, artifact) in enumerate(zip(starts, labels, flags), 1):\n", " session_trial += 1\n", " start0 = int(start) - 1\n", " raw6 = signal[start0:start0 + 1500, :22]\n", " _require(raw6.shape == (1500, 22) and np.isfinite(raw6).all(), \"Invalid EEG epoch\")\n", " filtered = sosfilt(sos, raw6, axis=0)[500:1500].T.copy()\n", " filtered -= filtered.mean(axis=1, keepdims=True)\n", " epochs.append(filtered)\n", " bandpower.append(_legacy_bandpower(raw6[500:1500]))\n", " region = \"source\" if session == 1 else (\"calibration_pool\" if task_run == 1 else \"common_test\")\n", " rows.append({\"subject\": subject, \"session\": session, \"filename\": filename,\n", " \"source_run\": source_run, \"task_run\": task_run, \"trial_in_run\": within_run,\n", " \"trial_in_session\": session_trial,\n", " \"trial_id\": f\"s{subject:02d}_d{session}_r{task_run:02d}_t{within_run:02d}\",\n", " \"source_start_sample_1based\": int(start), \"source_start_sample_0based\": start0,\n", " \"label\": int(label), \"artifact\": int(artifact),\n", " \"accepted\": int(artifact == 0), \"region\": region})\n", " _require(task_run == 6 and session_trial == 288, \"Expected six runs and 288 trials/session\")\n", " ledger = pd.DataFrame(rows)\n", " _require(len(ledger) == 576 and ledger.trial_id.is_unique, \"Invalid ledger\")\n", " for session in (1, 2):\n", " counts = ledger.loc[ledger.session == session].groupby(\"label\").size().to_dict()\n", " _require(counts == {label: 72 for label in LABELS}, \"Unexpected original class counts\")\n", " return {\"ledger\": ledger, \"epochs\": np.asarray(epochs),\n", " \"bandpower\": np.asarray(bandpower), \"source_files\": files}" ] }, { "cell_type": "code", "execution_count": 9, "id": "53d506ce", "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "def _invsqrt(matrix):\n", " values, vectors = np.linalg.eigh((matrix + matrix.T) / 2.0)\n", " _require(np.isfinite(values).all() and np.min(values) > 0, \"Covariance is not SPD\")\n", " floor = np.max(values) * 1e-12\n", " return (vectors * np.maximum(values, floor) ** -0.5) @ vectors.T" ] }, { "cell_type": "code", "execution_count": 10, "id": "ba2b0740", "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "def _congruence(covariances, whitening):\n", " result = whitening @ covariances @ whitening.T\n", " return (result + result.transpose(0, 2, 1)) / 2.0" ] }, { "cell_type": "code", "execution_count": 11, "id": "25c04c25", "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "def _lr():\n", " return LogisticRegression(C=1.0, solver=\"lbfgs\", max_iter=3000,\n", " tol=1e-6, class_weight=None)" ] }, { "cell_type": "code", "execution_count": 12, "id": "6159d668", "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "def _fit(estimator, X, y, audit, subject, stage, trial_ids,\n", " budget=None, sample_weight=None):\n", " \"\"\"Fail on convergence warnings; record every fitting/reference input ID.\"\"\"\n", " with warnings.catch_warnings(record=True) as captured:\n", " warnings.simplefilter(\"error\", ConvergenceWarning)\n", " with mne.use_log_level(\"ERROR\"):\n", " if sample_weight is None:\n", " estimator.fit(X, y)\n", " else:\n", " estimator.fit(X, y, sample_weight=sample_weight)\n", " entry = {\"subject\": subject, \"stage\": stage, \"budget\": budget,\n", " \"n_rows\": len(trial_ids), \"trial_ids\": list(trial_ids),\n", " \"n_iter\": np.asarray(getattr(estimator, \"n_iter_\", [])).tolist(),\n", " \"warnings\": [str(item.message) for item in captured]}\n", " if sample_weight is not None:\n", " entry[\"sample_weight_sum\"] = float(np.sum(sample_weight))\n", " audit.append(entry)\n", " return estimator" ] }, { "cell_type": "code", "execution_count": 13, "id": "b730006f", "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "def _features(covariances, tangent, scaler):\n", " return scaler.transform(tangent.transform(covariances))" ] }, { "cell_type": "code", "execution_count": 14, "id": "85e08261", "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "def _write_json(path, value):\n", " Path(path).write_text(json.dumps(value, indent=2, allow_nan=False) + \"\\n\", encoding=\"utf-8\")" ] }, { "cell_type": "code", "execution_count": 15, "id": "1770087c", "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "def run_study(data_dir, output_dir):\n", " \"\"\"Execute fixed development methods and save auditable, EEG-free results.\n", "\n", " Returns DataFrames under scores, predictions, trial_ledger,\n", " calibration_counts, uncertainty, leave_one_participant_out; metadata is a\n", " dictionary. Each table is saved under its key plus .csv; metadata is saved\n", " as access_audit.json. Calibration counts contain one row/method/budget/person.\n", " \"\"\"\n", " output_dir = Path(output_dir)\n", " output_dir.mkdir(parents=True, exist_ok=True)\n", " audit, ledgers, calibration_counts, scores, frames, files = [], [], [], [], [], []\n", " for subject in SUBJECTS:\n", " print(f\"Participant {subject}/4: loading verified recordings and fitting fixed methods\", flush=True)\n", " data = load_participant(subject, data_dir)\n", " ledger, epochs, bandpower = data[\"ledger\"], data[\"epochs\"], data[\"bandpower\"]\n", " ledgers.append(ledger)\n", " files.extend(data[\"source_files\"])\n", " source = ledger.index[(ledger.region == \"source\") & (ledger.accepted == 1)].to_numpy()\n", " test = ledger.index[(ledger.region == \"common_test\") & (ledger.accepted == 1)].to_numpy()\n", " pool = ledger.index[ledger.region == \"calibration_pool\"].to_numpy()\n", " source_ids = ledger.loc[source, \"trial_id\"].tolist()\n", " test_ids = ledger.loc[test, \"trial_id\"].tolist()\n", " ys, yt = ledger.loc[source, \"label\"].to_numpy(), ledger.loc[test, \"label\"].to_numpy()\n", " _require(set(ys) == set(LABELS) and set(yt) == set(LABELS), \"Missing source/test class\")\n", " _require(set(ledger.loc[test, \"task_run\"]) == {2, 3, 4, 5, 6}, \"Expected later five test runs\")\n", " _require(len(pool) == 48 and not set(source_ids).intersection(test_ids), \"Source/test split failure\")\n", "\n", " def fit(estimator, X, y, stage, ids=source_ids, budget=None, sample_weight=None):\n", " return _fit(estimator, X, y, audit, subject, stage, ids, budget, sample_weight)\n", "\n", " bp_scaler = fit(StandardScaler(), bandpower[source], None, \"bandpower_scaler\")\n", " bp_model = fit(LinearDiscriminantAnalysis(solver=\"lsqr\", shrinkage=\"auto\"),\n", " bp_scaler.transform(bandpower[source]), ys, \"bandpower_LDA\")\n", " fixed = {\"bandpower_LDA\": bp_model.predict(bp_scaler.transform(bandpower[test]))}\n", " csp = fit(CSP(n_components=8, reg=\"oas\", cov_est=\"epoch\", rank=\"full\", log=True,\n", " norm_trace=False, component_order=\"mutual_info\"), epochs[source], ys, \"CSP\")\n", " csp_model = fit(LinearDiscriminantAnalysis(solver=\"lsqr\", shrinkage=\"auto\"),\n", " csp.transform(epochs[source]), ys, \"CSP_LDA\")\n", " fixed[\"CSP_LDA\"] = csp_model.predict(csp.transform(epochs[test]))\n", "\n", " # Each covariance depends only on that trial; no cross-test fitting.\n", " covariance = Covariances(estimator=\"oas\")\n", " cs = covariance.transform(epochs[source])\n", " ct = covariance.transform(epochs[test])\n", " raw_tangent = fit(TangentSpace(metric=\"riemann\", tsupdate=False), cs, None, \"raw_tangent_reference\")\n", " raw_source = raw_tangent.transform(cs)\n", " raw_scaler = fit(StandardScaler(), raw_source, None, \"raw_tangent_scaler\")\n", " raw_lr = fit(_lr(), raw_scaler.transform(raw_source), ys, \"raw_tangent_LR\")\n", " fixed[\"raw_tangent_LR\"] = raw_lr.predict(_features(ct, raw_tangent, raw_scaler))\n", "\n", " ws = _invsqrt(cs.mean(axis=0))\n", " audit.append({\"subject\": subject, \"stage\": \"source_whitening_reference\", \"budget\": None,\n", " \"n_rows\": len(source), \"trial_ids\": source_ids, \"labels_used\": False})\n", " aligned_cs = _congruence(cs, ws)\n", " _require(np.allclose(aligned_cs.mean(axis=0), np.eye(22), atol=1e-7), \"Source recentering failed\")\n", " tangent = fit(TangentSpace(metric=\"riemann\", tsupdate=False), aligned_cs, None, \"ablation_tangent_reference\")\n", " source_features = tangent.transform(aligned_cs)\n", " scaler = fit(StandardScaler(), source_features, None, \"ablation_scaler\")\n", " zs = scaler.transform(source_features)\n", " base_lr = fit(_lr(), zs, ys, \"ablation_source_LR\")\n", " unaligned_test = _features(_congruence(ct, ws), tangent, scaler)\n", " frozen_prediction = base_lr.predict(unaligned_test)\n", "\n", " for budget in BUDGETS:\n", " prefix = pool[:budget]\n", " calibration = prefix[ledger.loc[prefix, \"accepted\"].to_numpy(dtype=bool)]\n", " ncal = len(calibration)\n", " cal_ids = ledger.loc[calibration, \"trial_id\"].tolist()\n", " yc = ledger.loc[calibration, \"label\"].to_numpy()\n", " _require(not set(cal_ids).intersection(test_ids), \"Calibration/test overlap\")\n", " _require(all(ledger.loc[calibration, \"trial_in_run\"] <= budget), \"Prefix budget exceeded\")\n", " class_counts = {f\"class_{label}_accepted\": int(np.sum(yc == label)) for label in LABELS}\n", " elapsed = 0.0 if budget == 0 else float(\n", " (ledger.loc[prefix[-1], \"source_start_sample_0based\"] - ledger.loc[prefix[0], \"source_start_sample_0based\"]) / 250 + 6.0)\n", " pred = dict(fixed)\n", " pred[\"frozen\"] = frozen_prediction\n", " iterations = {\"bandpower_LDA\": None, \"CSP_LDA\": None,\n", " \"raw_tangent_LR\": int(np.max(raw_lr.n_iter_)), \"frozen\": int(np.max(base_lr.n_iter_))}\n", " alpha = ncal / (ncal + 48.0) if ncal else 0.0\n", " if ncal == 0:\n", " for method in (\"alignment_only\", \"update_only\", \"combined\"):\n", " pred[method] = frozen_prediction.copy()\n", " iterations[method] = int(np.max(base_lr.n_iter_))\n", " else:\n", " cc = covariance.transform(epochs[calibration])\n", " wc = _invsqrt(cc.mean(axis=0))\n", " audit.append({\"subject\": subject, \"stage\": \"target_alignment_reference\", \"budget\": budget,\n", " \"n_rows\": ncal, \"trial_ids\": cal_ids, \"labels_used\": False})\n", " aligned_test = _features(_congruence(ct, wc), tangent, scaler)\n", " pred[\"alignment_only\"] = base_lr.predict(aligned_test)\n", " iterations[\"alignment_only\"] = int(np.max(base_lr.n_iter_))\n", " weights = np.r_[np.full(len(source), 1.0 - alpha), np.full(ncal, alpha * len(source) / ncal)]\n", " _require(np.isclose(weights.sum(), len(source)), \"Supervised weights must sum to Ns\")\n", " for method, whitening, test_features in (\n", " (\"update_only\", ws, unaligned_test), (\"combined\", wc, aligned_test)):\n", " zc = _features(_congruence(cc, whitening), tangent, scaler)\n", " updated = fit(_lr(), np.vstack([zs, zc]), np.r_[ys, yc], method,\n", " ids=source_ids + cal_ids, budget=budget, sample_weight=weights)\n", " pred[method] = updated.predict(test_features)\n", " iterations[method] = int(np.max(updated.n_iter_))\n", " if budget == 0:\n", " _require(all(np.array_equal(pred[\"frozen\"], pred[m]) for m in ABLATION_METHODS),\n", " \"Zero-budget ablation predictions differ\")\n", " for method in METHODS:\n", " labels_used = ncal if method in (\"update_only\", \"combined\") else 0\n", " target_used = ncal if method in (\"alignment_only\", \"update_only\", \"combined\") else 0\n", " calibration_counts.append({\"subject\": subject, \"method\": method, \"budget\": budget,\n", " \"available_original_trials\": len(prefix), \"accepted_trials\": ncal,\n", " \"flagged_exclusions\": len(prefix) - ncal,\n", " \"target_calibration_trials_used\": target_used, \"calibration_labels_used\": labels_used,\n", " \"all_four_classes_present\": set(yc) == set(LABELS),\n", " \"prefix_elapsed_seconds_to_last_epoch_end\": elapsed, **class_counts})\n", " scores.append({\"subject\": subject, \"method\": method, \"budget\": budget,\n", " \"n_source\": len(source), \"n_test\": len(test), \"accepted_calibration_available\": ncal,\n", " \"target_calibration_trials_used\": target_used, \"calibration_labels_used\": labels_used,\n", " \"supervised_target_total_weight_fraction\": alpha if labels_used else 0.0,\n", " \"accuracy\": float(accuracy_score(yt, pred[method])),\n", " \"balanced_accuracy\": float(balanced_accuracy_score(yt, pred[method])),\n", " \"correct\": int(np.sum(yt == pred[method])), \"classifier_n_iter\": iterations[method]})\n", " frame = ledger.loc[test, [\"subject\", \"session\", \"task_run\", \"trial_in_run\",\n", " \"trial_in_session\", \"trial_id\", \"label\"]].copy()\n", " frame[\"prediction\"], frame[\"method\"], frame[\"budget\"] = pred[method], method, budget\n", " frames.append(frame)\n", "\n", " allowed = set(source_ids) | set(ledger.loc[pool, \"trial_id\"])\n", " for entry in (record for record in audit if record[\"subject\"] == subject):\n", " _require(set(entry[\"trial_ids\"]).issubset(allowed), \"Fit accessed unallowed data\")\n", " _require(not set(entry[\"trial_ids\"]).intersection(test_ids), \"Common test trial in fit audit\")\n", " if entry.get(\"budget\") is not None:\n", " allowed_prefix = set(ledger.loc[pool[:entry[\"budget\"]], \"trial_id\"])\n", " _require(set(entry[\"trial_ids\"]).issubset(set(source_ids) | allowed_prefix),\n", " \"Fit access exceeded original prefix budget\")\n", "\n", " score_frame = pd.DataFrame(scores)\n", " predictions = pd.concat(frames, ignore_index=True)\n", " uncertainty, leave_one_out = summarize_uncertainty(predictions, score_frame)\n", " tables = {\"scores\": score_frame, \"predictions\": predictions,\n", " \"trial_ledger\": pd.concat(ledgers, ignore_index=True),\n", " \"calibration_counts\": pd.DataFrame(calibration_counts),\n", " \"uncertainty\": uncertainty, \"leave_one_participant_out\": leave_one_out}\n", " for name, table in tables.items():\n", " table.to_csv(output_dir / f\"{name}.csv\", index=False)\n", " metadata = {\n", " \"study\": \"002\", \"dataset\": \"BNCI2014_001\", \"created_utc\": datetime.now(timezone.utc).isoformat(),\n", " \"subjects_read\": list(SUBJECTS), \"reserved_subjects_never_read\": [5, 6, 7, 8, 9],\n", " \"budgets_original_chronological_trials\": list(BUDGETS), \"methods\": list(METHODS),\n", " \"primary_comparison\": \"combined minus update_only at 24 original prefix trials\",\n", " \"secondary_comparisons\": [\"combined minus frozen at 24\", \"combined minus alignment_only at 24\"],\n", " \"source_files\": files,\n", " \"versions\": {\"python\": platform.python_version(), \"numpy\": np.__version__, \"pandas\": pd.__version__,\n", " \"scipy\": scipy.__version__, \"scikit_learn\": sklearn.__version__,\n", " \"mne\": mne.__version__, \"pyriemann\": pyriemann.__version__},\n", " \"access_rules\": {\n", " \"source\": \"All accepted day-one trials, independently per participant\",\n", " \"calibration\": \"Accepted trials within original first-day-two-run prefix; no replacement of flagged trials\",\n", " \"test\": \"Same accepted trials in day-two task runs 2--6 for all methods and budgets\",\n", " \"test_covariances\": \"Each OAS covariance constructed independently from the current trial; no cross-test estimation\",\n", " \"tangent_and_scaler\": \"Source-fitted once per representation; tsupdate=False; unchanged at every budget\",\n", " \"target_alignment\": \"Mean OAS covariance of accepted prefix only; OAS not recomputed after congruence\",\n", " \"preprocessing\": \"250 Hz, first 22 EEG; causal fourth-order Butterworth 8--30 Hz over [0,6), zero state per trial; crop [2,6), demean per channel\",\n", " \"bandpower\": \"Study001 unfiltered raw [2,6) Welch 44 log10 bandpower features\",\n", " \"supervised_weights\": \"alpha=n/(n+48); each source 1-alpha; each target alpha*Ns/n; sum Ns\",\n", " \"zero_budget\": \"All four ablation arms use source whitening and the same frozen classifier\"},\n", " \"bootstrap\": {\"repetitions\": 5000, \"seed\": 20261108, \"unit\": \"whole test task run\",\n", " \"conditional_on\": \"These four participants, their two sessions, observed calibration prefixes, and fitted models\",\n", " \"aggregate\": \"Equal mean participant BA; independently resampled runs within each fixed participant\",\n", " \"limitations\": \"No participant resampling, refitting, new-day uncertainty, or confirmatory population inference; unadjusted percentile intervals\"},\n", " \"fit_access_audit\": audit,\n", " \"engine_sha256\": _sha256(__file__) if \"__file__\" in globals() and Path(__file__).is_file() else None,\n", " }\n", " _write_json(output_dir / \"access_audit.json\", metadata)\n", " return {**tables, \"metadata\": metadata}" ] }, { "cell_type": "code", "execution_count": 16, "id": "a89e3c3f", "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "def summarize_uncertainty(predictions, scores=None, repetitions=5000, seed=20261108):\n", " \"\"\"Paired conditional whole-run bootstrap at the fixed 24-trial budget.\n", "\n", " All comparisons share run draws within person. Aggregate means use four\n", " fixed people and independent within-person run draws; no person resampling.\n", " \"\"\"\n", " selected = predictions.loc[predictions.budget == 24].copy()\n", " _require(set(selected.subject) == set(SUBJECTS), \"Exactly four development people required\")\n", " rng = np.random.default_rng(seed)\n", " comparators = (\"update_only\", \"frozen\", \"alignment_only\")\n", " points, bootstraps, rows = {}, {}, []\n", "\n", " def append_interval(subject, point, bootstrap):\n", " for comparator in comparators:\n", " ci, mi = METHODS.index(\"combined\"), METHODS.index(comparator)\n", " low, high = np.quantile(bootstrap[:, ci] - bootstrap[:, mi], [.025, .975])\n", " rows.append({\"subject\": subject, \"budget\": 24,\n", " \"contrast\": f\"combined_minus_{comparator}\",\n", " \"prespecified_role\": \"primary\" if comparator == \"update_only\" else \"secondary\",\n", " \"estimate_pp\": float(100 * (point[ci] - point[mi])),\n", " \"ci_low_pp\": float(100 * low), \"ci_high_pp\": float(100 * high),\n", " \"repetitions\": repetitions,\n", " \"interval_type\": \"conditional paired whole-run percentile; unadjusted\"})\n", "\n", " for subject in SUBJECTS:\n", " person = selected.loc[selected.subject == subject]\n", " paired = person.pivot(index=[\"task_run\", \"trial_id\", \"label\"],\n", " columns=\"method\", values=\"prediction\").reset_index()\n", " _require(paired[list(METHODS)].notna().all().all(), \"Unpaired predictions\")\n", " run_ids = sorted(paired.task_run.unique())\n", " _require(run_ids == [2, 3, 4, 5, 6], \"Expected five common test runs\")\n", " totals = np.zeros((5, 4), dtype=int)\n", " correct = np.zeros((5, len(METHODS), 4), dtype=int)\n", " for ri, run in enumerate(run_ids):\n", " for ci, label in enumerate(LABELS):\n", " group = paired.loc[(paired.task_run == run) & (paired.label == label)]\n", " totals[ri, ci] = len(group)\n", " for mi, method in enumerate(METHODS):\n", " correct[ri, mi, ci] = int(np.sum(group[method] == label))\n", " _require((totals > 0).all(), \"A test run lacks a class\")\n", " draws = rng.integers(0, 5, size=(repetitions, 5))\n", " boot_ba = (correct[draws].sum(axis=1) / totals[draws].sum(axis=1)[:, None, :]).mean(axis=2)\n", " point_ba = (correct.sum(axis=0) / totals.sum(axis=0)[None, :]).mean(axis=1)\n", " points[subject], bootstraps[subject] = point_ba, boot_ba\n", " if scores is not None:\n", " observed = scores.loc[(scores.subject == subject) & (scores.budget == 24)].set_index(\"method\")\n", " _require(np.allclose(point_ba, observed.loc[list(METHODS), \"balanced_accuracy\"]),\n", " \"Bootstrap point and observed score differ\")\n", " append_interval(subject, point_ba, boot_ba)\n", " append_interval(\"mean\", np.mean(list(points.values()), axis=0),\n", " np.mean(list(bootstraps.values()), axis=0))\n", " ci, mi = METHODS.index(\"combined\"), METHODS.index(\"update_only\")\n", " leave_one_out = [{\"omitted_subject\": omitted, \"budget\": 24,\n", " \"contrast\": \"combined_minus_update_only\",\n", " \"estimate_pp\": float(100 * np.mean([points[s][ci] - points[s][mi] for s in SUBJECTS if s != omitted])),\n", " \"interpretation\": \"descriptive sensitivity; not independent validation\"} for omitted in SUBJECTS]\n", " return pd.DataFrame(rows), pd.DataFrame(leave_one_out)" ] }, { "cell_type": "markdown", "id": "4878c453", "metadata": {}, "source": [ "## Data and execution\n", "\n", "Run the complete fixed study. All candidate settings are specified before test scoring. Progress is printed once per participant. Assertions check the data boundaries and zero-budget equivalence; convergence failures stop execution." ] }, { "cell_type": "code", "execution_count": 17, "id": "239de1a4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Participant 1/4: loading verified recordings and fitting fixed methods\n", "Participant 2/4: loading verified recordings and fitting fixed methods\n", "Participant 3/4: loading verified recordings and fitting fixed methods\n", "Participant 4/4: loading verified recordings and fitting fixed methods\n", "Study computation completed.\n" ] } ], "source": [ "with threadpool_limits(limits=1):\n", " study_result = run_study(DATA_DIRECTORY, OUTPUT_DIRECTORY)\n", "print('Study computation completed.')" ] }, { "cell_type": "code", "execution_count": 18, "id": "aa3366bc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " subject class_1_accepted ... class_3_accepted class_4_accepted\n", "21 1 7 ... 3 5\n", "56 2 8 ... 3 6\n", "91 3 7 ... 3 6\n", "126 4 8 ... 3 3\n", "\n", "[4 rows x 5 columns]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Result tables: uncertainty.csv, trial_ledger.csv, leave_one_participant_out.csv, predictions.csv, scores.csv, calibration_counts.csv\n" ] } ], "source": [ "import pandas as pd\n", "scores = pd.read_csv(OUTPUT_DIRECTORY / 'scores.csv')\n", "predictions = pd.read_csv(OUTPUT_DIRECTORY / 'predictions.csv')\n", "calibration = pd.read_csv(OUTPUT_DIRECTORY / 'calibration_counts.csv')\n", "print('Result tables:', ', '.join(p.name for p in OUTPUT_DIRECTORY.glob('*.csv')))\n", "prefix_counts = calibration.drop_duplicates(['subject','budget'])\n", "display(prefix_counts.pivot(index='subject', columns='budget', values='accepted_trials').rename_axis(columns='Original budget'))\n", "display(scores.query(\"budget == 0 and method == 'frozen'\")[[\"subject\",\"n_source\",\"n_test\"]])\n", "display(prefix_counts.query('budget == 24')[[\"subject\",\"class_1_accepted\",\"class_2_accepted\",\"class_3_accepted\",\"class_4_accepted\"]])" ] }, { "cell_type": "markdown", "id": "3e14ecfd", "metadata": {}, "source": [ "## Results\n", "\n", "Balanced accuracy is the mean recall across four classes. The dashed horizontal reference is 25%; this is a reference for uniform guessing, not an empirical significance threshold. The curves show a single observed calibration prefix per participant and budget, with later test trials held constant." ] }, { "cell_type": "code", "execution_count": 19, "id": "16545f2c", "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "\"\"\"Portable plotting for Study 002; inputs are reviewed result tables.\"\"\"\n", "\n", "from pathlib import Path\n", "\n", "import numpy as np\n", "\n", "import pandas as pd\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "DISPLAY_LABELS = {'bandpower_LDA':'Bandpower + LDA','CSP_LDA':'CSP + LDA','raw_tangent_LR':'Raw tangent + LR', 'frozen':'Frozen tangent decoder','alignment_only':'Alignment only','update_only':'Supervised update only','combined':'Alignment + update'}\n", "\n", "COLORS = {'frozen':'#5b6571','alignment_only':'#b58119','update_only':'#276a9f','combined':'#ae4c36'}\n", "\n", "MARKERS = {'frozen':'s','alignment_only':'^','update_only':'o','combined':'D'}\n", "\n", "ARMS=['frozen','alignment_only','update_only','combined']" ] }, { "cell_type": "code", "execution_count": 20, "id": "c5f2748a", "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "def configure_style():\n", " plt.rcParams.update({'font.family':'DejaVu Sans','font.size':11,'axes.titlesize':13,'axes.labelsize':11,'axes.spines.top':False,'axes.spines.right':False,'savefig.dpi':160,'figure.facecolor':'white','axes.facecolor':'white'})" ] }, { "cell_type": "code", "execution_count": 21, "id": "10b4ad85", "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "def calibration_curves(scores, output_dir):\n", " configure_style(); output_dir=Path(output_dir); output_dir.mkdir(parents=True,exist_ok=True)\n", " fig,axes=plt.subplots(2,2,figsize=(12,8),sharex=True,sharey=True)\n", " for subject,ax in zip([1,2,3,4],axes.flat):\n", " for method in ARMS:\n", " rows=scores.query('subject == @subject and method == @method').sort_values('budget')\n", " ax.plot(rows.budget,100*rows.balanced_accuracy,label=DISPLAY_LABELS[method],color=COLORS[method],marker=MARKERS[method],linewidth=1.8,markersize=5,linestyle='--' if method=='frozen' else '-')\n", " ax.axhline(25,color='#aaaaaa',linestyle=':',linewidth=1)\n", " ax.set(title=f'Participant {subject}',ylim=(0,100),xticks=[0,8,16,24,48],yticks=[0,25,50,75,100])\n", " ax.grid(axis='y',alpha=.18)\n", " for ax in axes[1]:ax.set_xlabel('Original calibration-trial budget')\n", " for ax in axes[:,0]:ax.set_ylabel('Balanced accuracy (%)')\n", " handles,labels=axes[0,0].get_legend_handles_labels()\n", " fig.legend(handles,labels,loc='lower center',bbox_to_anchor=(.5,.045),ncol=2,frameon=False)\n", " fig.suptitle('Study 002: calibration budget and later-session performance',fontsize=16,y=.98)\n", " fig.text(.5,.015,'Development participants 1–4 · Same accepted test trials in runs 2–6 at every budget\\nLines connect tested budgets; they do not imply guaranteed improvement. Dashed horizontal reference: 25%.',ha='center',fontsize=9,color='#555555')\n", " fig.subplots_adjust(left=.09,right=.98,top=.9,bottom=.2,wspace=.13,hspace=.25)\n", " fig.savefig(output_dir/'calibration_curves.png',bbox_inches='tight')\n", " return fig" ] }, { "cell_type": "code", "execution_count": 22, "id": "af44be59", "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "def aggregate_curves(scores, output_dir):\n", " configure_style();output_dir=Path(output_dir)\n", " agg=scores.groupby(['method','budget'],as_index=False).balanced_accuracy.mean()\n", " fig,ax=plt.subplots(figsize=(10,5.6))\n", " for method in ARMS:\n", " rows=agg.query('method == @method').sort_values('budget')\n", " ax.plot(rows.budget,100*rows.balanced_accuracy,label=DISPLAY_LABELS[method],color=COLORS[method],marker=MARKERS[method],linewidth=2,linestyle='--' if method=='frozen' else '-')\n", " ax.set(xlabel='Original calibration-trial budget',ylabel='Mean balanced accuracy (%)',ylim=(0,100),xticks=[0,8,16,24,48],yticks=[0,25,50,75,100])\n", " ax.axhline(25,color='#aaaaaa',linestyle=':',linewidth=1);ax.grid(axis='y',alpha=.18)\n", " ax.set_title('Calibration curves: equal-weight mean across four development participants',pad=17)\n", " ax.legend(frameon=False,loc='upper left',ncol=2)\n", " fig.text(.5,.02,'Point estimates on the same later-session trials; participant-level variation is shown in the companion figure.',ha='center',fontsize=9,color='#555555')\n", " fig.tight_layout(rect=(0,.06,1,1));fig.savefig(output_dir/'mean_calibration_curve.png',bbox_inches='tight')\n", " return fig" ] }, { "cell_type": "code", "execution_count": 23, "id": "5df60622", "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "def primary_comparison(intervals, output_dir):\n", " \"\"\"Expected columns: subject, contrast, estimate_pp, ci_low_pp, ci_high_pp.\"\"\"\n", " configure_style();output_dir=Path(output_dir)\n", " rows=intervals.query(\"contrast == 'combined_minus_update_only'\").copy()\n", " wanted=['1','2','3','4','mean'];rows['key']=rows.subject.astype(str).str.replace('.0','',regex=False)\n", " rows=rows.set_index('key').loc[wanted]\n", " fig,ax=plt.subplots(figsize=(10,5.5));ys=np.arange(5)[::-1]\n", " for y,(key,row) in zip(ys,rows.iterrows()):\n", " color='#183a2c' if key=='mean' else '#276a9f'\n", " ax.hlines(y,row.ci_low_pp,row.ci_high_pp,color=color,lw=2)\n", " ax.plot(row.estimate_pp,y,'D' if key=='mean' else 'o',color=color,markersize=7)\n", " ax.annotate(f'{row.estimate_pp:+.2f} [{row.ci_low_pp:+.2f}, {row.ci_high_pp:+.2f}]',xy=(1.02,y),xycoords=('axes fraction','data'),va='center',fontsize=10,color=color)\n", " ax.axvline(0,color='#8b8b8b',linestyle='--',linewidth=1)\n", " ax.set(yticks=ys,yticklabels=['Participant 1','Participant 2','Participant 3','Participant 4','Four-person mean'],xlabel='Combined minus supervised update only (percentage points)',ylim=(-.6,4.6))\n", " low=min(rows.ci_low_pp.min(),0);high=max(rows.ci_high_pp.max(),0);margin=max(1,(high-low)*.08);ax.set_xlim(low-margin,high+margin)\n", " ax.grid(axis='x',alpha=.15);ax.set_title('Primary comparison at 24 original calibration trials',pad=18)\n", " fig.text(.5,.025,'95% paired run-bootstrap intervals, conditional on these four people, fitted models,\\ncalibration prefixes and one day-two session. These are not population or future-day intervals.',ha='center',fontsize=9,color='#555555')\n", " fig.subplots_adjust(left=.18,right=.68,bottom=.22,top=.87)\n", " fig.savefig(output_dir/'primary_comparison.png',bbox_inches='tight')\n", " return fig" ] }, { "cell_type": "code", "execution_count": 24, "id": "cf1c17cf", "metadata": {}, "outputs": [ { "data": { "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "calibration_curves(scores, OUTPUT_DIRECTORY)\n", "plt.show()\n", "aggregate_curves(scores, OUTPUT_DIRECTORY)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "ddc9648e", "metadata": {}, "source": [ "### Exact comparison at the primary 24-trial budget\n", "\n", "The four-person mean gives each participant equal weight, irrespective of retained trial count. The additional frozen baselines provide context; the predeclared primary contrast is combined minus update-only." ] }, { "cell_type": "code", "execution_count": 25, "id": "1dff43a4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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subject1234Equal-person mean
CSP + LDA74.8655.4374.5560.9966.46
Alignment only78.0952.5073.7558.9865.83
Bandpower + LDA59.7635.1760.8751.9551.94
Alignment + update78.5249.0174.6464.7366.73
Frozen tangent decoder77.3950.4780.4355.7466.00
Raw tangent + LR78.2750.4779.9855.7466.11
Supervised update only77.4949.4276.4566.5167.47
\n", "
" ], "text/plain": [ "subject 1 2 3 4 Equal-person mean\n", "CSP + LDA 74.86 55.43 74.55 60.99 66.46\n", "Alignment only 78.09 52.50 73.75 58.98 65.83\n", "Bandpower + LDA 59.76 35.17 60.87 51.95 51.94\n", "Alignment + update 78.52 49.01 74.64 64.73 66.73\n", "Frozen tangent decoder 77.39 50.47 80.43 55.74 66.00\n", "Raw tangent + LR 78.27 50.47 79.98 55.74 66.11\n", "Supervised update only 77.49 49.42 76.45 66.51 67.47" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "primary_table = 100*scores.query('budget == 24').pivot(index='method', columns='subject', values='balanced_accuracy')\n", "primary_table['Equal-person mean'] = primary_table.mean(axis=1)\n", "primary_table.index = [DISPLAY_LABELS.get(m,m) for m in primary_table.index]\n", "display(primary_table.round(2))" ] }, { "cell_type": "markdown", "id": "33fbad3e", "metadata": {}, "source": [ "### Paired uncertainty\n", "\n", "The 95% percentile intervals resample whole test runs, preserving pairing between methods. The four-person mean resamples runs independently within each fixed participant. These are conditional intervals for the observed people, calibration prefixes, fitted models and one test session; they are not intervals for future days or a participant population. Five test runs per participant provide limited resampling information." ] }, { "cell_type": "code", "execution_count": 26, "id": "8ab281e9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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subjectbudgetcontrastprespecified_roleestimate_ppci_low_ppci_high_pprepetitionsinterval_type
0124combined_minus_update_onlyprimary1.030-2.3904.1205000conditional paired whole-run percentile; unadj...
1124combined_minus_frozensecondary1.125-2.5005.1035000conditional paired whole-run percentile; unadj...
2124combined_minus_alignment_onlysecondary0.424-2.6103.0325000conditional paired whole-run percentile; unadj...
3224combined_minus_update_onlyprimary-0.402-2.9031.7705000conditional paired whole-run percentile; unadj...
4224combined_minus_frozensecondary-1.451-6.7863.9695000conditional paired whole-run percentile; unadj...
5224combined_minus_alignment_onlysecondary-3.491-7.184-0.1635000conditional paired whole-run percentile; unadj...
6324combined_minus_update_onlyprimary-1.808-4.0330.6525000conditional paired whole-run percentile; unadj...
7324combined_minus_frozensecondary-5.781-11.7810.2195000conditional paired whole-run percentile; unadj...
8324combined_minus_alignment_onlysecondary0.893-3.1393.3975000conditional paired whole-run percentile; unadj...
9424combined_minus_update_onlyprimary-1.773-5.5461.3505000conditional paired whole-run percentile; unadj...
10424combined_minus_frozensecondary8.997-0.71116.5945000conditional paired whole-run percentile; unadj...
11424combined_minus_alignment_onlysecondary5.7543.5668.2035000conditional paired whole-run percentile; unadj...
12mean24combined_minus_update_onlyprimary-0.738-2.2920.6575000conditional paired whole-run percentile; unadj...
13mean24combined_minus_frozensecondary0.722-2.5833.6895000conditional paired whole-run percentile; unadj...
14mean24combined_minus_alignment_onlysecondary0.895-0.6742.3125000conditional paired whole-run percentile; unadj...
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" ], "text/plain": [ " subject ... interval_type\n", "0 1 ... conditional paired whole-run percentile; unadj...\n", "1 1 ... conditional paired whole-run percentile; unadj...\n", "2 1 ... conditional paired whole-run percentile; unadj...\n", "3 2 ... conditional paired whole-run percentile; unadj...\n", "4 2 ... conditional paired whole-run percentile; unadj...\n", "5 2 ... conditional paired whole-run percentile; unadj...\n", "6 3 ... conditional paired whole-run percentile; unadj...\n", "7 3 ... conditional paired whole-run percentile; unadj...\n", "8 3 ... conditional paired whole-run percentile; unadj...\n", "9 4 ... conditional paired whole-run percentile; unadj...\n", "10 4 ... conditional paired whole-run percentile; unadj...\n", "11 4 ... conditional paired whole-run percentile; unadj...\n", "12 mean ... conditional paired whole-run percentile; unadj...\n", "13 mean ... conditional paired whole-run percentile; unadj...\n", "14 mean ... conditional paired whole-run percentile; unadj...\n", "\n", "[15 rows x 9 columns]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "intervals = pd.read_csv(OUTPUT_DIRECTORY / 'uncertainty.csv')\n", "display(intervals.round(3))\n", "primary_comparison(intervals, OUTPUT_DIRECTORY)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "51d927f2", "metadata": {}, "source": [ "## Takeaways\n", "\n", "1. **The added alignment component has not earned a performance claim.** The primary estimate is negative and its conditional interval includes zero. Leaving out any one participant keeps the primary mean negative (range -1.33 to -0.38 pp); these are descriptive sensitivity checks.\n", "2. **Updating can help some people and hurt others.** At 24 trials, update-only averages 67.47% against the frozen decoder's 66.00%. The mean gain is descriptive and is uneven across participants. The largest mean among tested update-only budgets is 70.17% at 48; this is a secondary curve observation, not an independently validated optimal budget.\n", "3. **Short-prefix alignment can fail badly.** Participant 3's seven accepted trials at the nominal eight-trial budget contained three left-hand, three right-hand, one feet, and no tongue trial. Unrepresentative class composition is one plausible contributor to the covariance shift; this experiment does not isolate the cause. Participants 1 and 2 also lacked tongue examples at this budget without the same collapse.\n", "4. **Established representations matter.** Frozen CSP–LDA (66.46%) and raw tangent–LR (66.11%) outperform bandpower–LDA (51.94%) on the same test trials. Their preprocessing and representation differ; this is not an isolated algorithm effect or a new-method claim.\n", "5. **Next research decision:** retain supervised update-only as the comparator to beat; investigate conservative, prefix-dependent covariance recentering on development data before freezing any new rule. Keep participants 5–9 reserved. More sessions and independent data are needed before claiming reliable calibration savings.\n", "\n", "This is an offline development ablation of existing ingredients. It does not establish a novel decoder, live control benefit, clinical usefulness, commercial readiness, or reduced real-world calibration time." ] }, { "cell_type": "markdown", "id": "480ffb09", "metadata": {}, "source": [ "## References and reproducibility\n", "\n", "- [BNCI2014_001 / BCI Competition IV 2a description](https://bnci-horizon-2020.eu/database/data-sets/001-2014/description.pdf)\n", "- [He & Wu: Euclidean-space data alignment](https://arxiv.org/abs/1808.05464)\n", "- [MNE CSP documentation](https://mne.tools/stable/generated/mne.decoding.CSP.html)\n", "- [pyRiemann TangentSpace documentation](https://pyriemann.readthedocs.io/en/latest/generated/pyriemann.tangentspace.TangentSpace.html)\n", "- [scikit-learn LogisticRegression documentation](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html)\n", "\n", "The accompanying study bundle contains the frozen protocol, source hashes, pinned package versions, standalone engine, plots, trial-level predictions, calibration ledger, results, and independent validation record. Public raw EEG is retrieved from the source rather than redistributed in the bundle." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "name": "python", "version": "3.12.14" }, "study_id": "Daybreak Neural 002" }, "nbformat": 4, "nbformat_minor": 5 }