Motor-imagery EEG synthetic stress experiment — 2026-11-03¶

Status: research-only synthetic software experiment. Not BCI efficacy evidence, human EEG classification, a benchmark on real devices, a shipped feature, a clinical/medical claim or an approved ADR.

Tracking: #696 — EEG/EOG feasibility (closed 2026-11-03), #692 — HCI lab, #790 — accessible workspace research, #817 — evaluation.

Existing research to reuse, not duplicate: P0/P1 artifact study, existing EEG/EOG synthetic replay, multimodal fixture schema (#699), 2026 brain interface feasibility report.

Executable artifacts: Synthetic motor-imagery stress script, 13 isolated regression tests.

Scientific question¶

The existing P0/P1 work tests burst detection and rejection of injected eye/muscle artifacts, not neural motor imagery. This separate, standalone experiment asks a software-only negative-control question:

When we invent a 10 Hz motor-cortex-like signal whose amplitude deliberately falls during a labelled "imagery" interval, will a causal rest-calibrated detector also fire when rest amplitude drifts or a sensor disconnects?

All signal shapes, amplitude changes, correlations, thresholds and scenarios were hand-authored. There are no human signals and no device recordings, so any apparent imagery detection is built into the generator. This is not a neural decoder and does not use/alter the existing HCI fixture contract.

Run and reproduce¶

The code uses Python standard library only, requires no hardware, network, model download or additional package.

python scripts/hci_eeg_mi_synthetic.py --seed 0
python scripts/hci_eeg_mi_synthetic.py --seed 0 --stop-at-s 33
python -m pytest tests/test_hci_eeg_mi_synthetic.py -q

Within a fully installed YazSes checkout, use its usual test runner instead:

uv run python -m pytest tests/test_hci_eeg_mi_synthetic.py -q

Stop is a simulated independent external stop, not a brain-triggered action. The experiment prints JSON with a synthetic-data notice and no OS action. It never uses an activation-source adapter or YazSes daemon code.

Generator — invented parameters, not physiological estimates¶

• 100 samples/second, two independent C3/C4-like oscillatory channels, plus separate EOG and EMG artifact streams.
• One 6-second rest calibration segment, then 13 short scripted intervals (8 seconds each): rest, two imagery blocks, amplitude-drift rest, blink, jaw-clench, speaking, missing data, flatline and recovery.
• 10 Hz sine/cosine amplitude 1.00 at rest, 0.30/0.32 during artificial imagery; 0.48 for drifted rest, which deliberately resembles imagery.
• Pseudorandom noise with a frozen per-run seed. No participants, real equipment, real field sessions or independent recording sessions.

Causal pseudo-online rule¶

• Extract 10 Hz quadrature power from trailing 1-second windows at 250 ms hops. No future-sample lookahead.
• Estimate initial baseline only during the 6-second calibration; require feature power less than 55% of it for two consecutive windows to emit a candidate proposal.
• Re-arm only after observing 80% of initial rest power. The emitted suppression score is a heuristic, not calibrated confidence/probability.
• Mark windows missing or nonfinite, recently flatlined, high EOG or high EMG as unusable and abstain. After a signal failure require a new observed rest before rearming.
• An external stop disables all later candidate proposals. No text, click, microphone or app action is ever performed.
• Ground-truth segment labels are only read by the post-hoc scorer, never by inference.

Local observed results — synthetic by construction¶

Ran in a standalone Python 3.13.5 environment; the figures are from the seed 0 script run:

Seeds 0–7 were also executed locally. Each had two artificial hits and one false candidate in drifted rest. This consistency reflects the fixed authored generator, not subject/session generalization or a confidence interval. In a real system, a false activation is much more consequential than a good-looking synthetic hit rate.

Tests: 13 of 13 new isolated Pytest cases passed locally. Checks include reproducibility, synthetic notices, mandatory false drift case, blink/clench/speaking/dropout/flatline refusal, causality under future-data mutation, independent stop, NaN/missing handling, inference independence from labels, CLI JSON output and bounded timestamps. The exact Git blob hashes of script/tests uploaded to the research branch matched locally tested files. Syntax compilation also passed.

Not run: full project test suite, Ruff/CI, real EEG, BrainFlow hardware, real participants, cross-session independent data or publication-grade analysis. Do not imply the 13 isolated tests establish whole-repository correctness.

Interpretation and design consequences¶

1. No practical EEG switch has been demonstrated. The detector merely recognizes a signal constructed for it, and fails on an equally constructed rest-drift case.
2. Baseline drift is a critical false-trigger risk. Avoid treating binary offline classification scores as real continuous-use reliability. Collect independent sessions, real rest activity and unfamiliar negative conditions before estimating safety.
3. Signal quality and stop must be fail-closed. A recent flatline window initially produced a false candidate; adding a short recent-sample quality check prevented that case in this synthetic test, but does not solve baseline drift or all unknown states.
4. Report proper denominators on real data. The scripted 88 seconds is not real usable idle exposure. Only a sufficiently long, predeclared prospective evaluation can estimate false activations per usable hour with uncertainty.
5. Do not confuse neural EEG with blink/jaw artifacts. The existing EMG/EOG paths remain the appropriate practical baseline; this is a separate experimental motor-imagery question.
6. Keep neural candidates out of product actions. No model, recording, calibration profile or signal stream may be uploaded or silently sent. ADR-030 (Accepted) governs; the biosignal-switch scope in ADR-v2-152 is still Proposed, so it is a boundary this experiment respects rather than a binding decision.

Next gate: independent neural dataset study, not yet started¶

Before any real motor-imagery inference is claimed:

1. Review a specific public EEG motor-imagery dataset (potentially PhysioNet EEG Motor Movement/Imagery or a MOABB-indexed dataset), its exact licensing, participant rights, channels, sample rates, true rest labels, recordings and session structure before downloading or training anything.
2. Freeze a participant/session-disjoint split. Never split overlapping windows from one continuous recording across train and test. Perform preprocessing, calibration and model fitting only on training data.
3. Compare always-rest with elementary bandpower/CSP and a regularized linear classifier; a covariance-based method is a secondary reference. Declare metrics and thresholds in advance, not after seeing the test set.
4. Perform causal replay and report misses, correct intentional cues, false candidates per usable idle exposure, latency p50/p95, abstention/quality-failure fraction, unknown-class and artifact negatives, sensor interruptions, session drift and performance on normal CPU.
5. No dataset may be interpreted as having measured speech/blink/head-motion rejection unless it explicitly contains those labelled controls. Missing classes must be reported as not tested, not as zero errors.
6. Human use of hardware, photosensitive stimuli, participant data or live interaction remains separately gated by review/consent, accessibility and safety requirements under #425/#427/#814, plus the accepted ADR-030 direction.

Recommendation: #696 is closed (2026-11-03), so this follow-up is not tracked on an open issue. Reopening #696, or opening a new research issue, is a maintainer decision. Until independent cross-session results and user-demand evidence exist, do not open a production neural-activation task, claim mind reading, or propose an ADR change.