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Physiological Workload ML Pipeline

Analysis repository for the OpenNeuro study ds007262 version 1.0.6, titled Cognitive Workload 8-level arithmetic (DOI 10.18112/openneuro.ds007262.v1.0.6). The codebase covers dataset acquisition, trial-table construction, QC, preprocessing, epoching, unimodal feature extraction, fused-table assembly, split-aware machine learning, confusion analysis, and publication-oriented reporting for this specific study snapshot.

Repository layout

  • analysis_pipeline/: executable stage scripts, pipeline configs, and supporting modules.
  • analysis_pipeline/config/: checked-in YAML profiles for reproducible runs.
  • scripts/: dataset download, end-to-end execution, and report/manuscript helpers.
  • docs/: GitHub-facing documentation plus manuscript handoff material.
  • data/: local BIDS dataset root (ignored).
  • analysis_pipeline/runs/: run-specific outputs (ignored).

This repository is intentionally separate from the OpenNeuro dataset record itself. Track code, configs, and documentation here; keep raw data and generated outputs local.

Quick start

1. Create the environment

python -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install --upgrade pip
python -m pip install -r requirements.txt

requirements.txt covers the classic ML and signal-processing stack. Install PyTorch separately if you plan to run deep Stage 6 models such as lstm1d, gru1d, cnn1d, or transformer.

2. Acquire the BIDS dataset

OpenNeuro dataset snapshot for this repository:

python .\scripts\download_bids.py `
  --dataset-id ds007262 `
  --snapshot 1.0.6 `
  --target .\data\bids_arithmetic

Direct archive URL:

python .\scripts\download_bids.py `
  --archive-url https://example.org/your_bids_archive.zip `
  --target .\data\bids_arithmetic

One-command download plus pipeline execution for ds007262 v1.0.6:

.\scripts\run_end_to_end.ps1 -DatasetId ds007262 -Snapshot 1.0.6 -ForceDownload

3. Run a checked-in profile

Default fixed-window profile:

python .\analysis_pipeline\run_pipeline.py `
  --config .\analysis_pipeline\config\pipeline_unified_classic_nn_baseline_preproc.yaml

Alternative overlap profile:

python .\analysis_pipeline\run_pipeline.py `
  --config .\analysis_pipeline\config\pipeline_unified_classic_nn_baseline_overlap3s_50pct_preproc.yaml

The checked-in profiles assume a local copy of OpenNeuro ds007262 v1.0.6 under data/bids_arithmetic. They write under analysis_pipeline/runs/<profile_name>/. Both set outputs.clean_start: true, so rerunning the same profile replaces that profile's run directory only. If you want to preserve an existing run, copy the YAML and change outputs.root or set clean_start: false.

4. Run stage subsets

Run through feature extraction:

python .\analysis_pipeline\run_pipeline.py `
  --config .\analysis_pipeline\config\pipeline_unified_classic_nn_baseline_preproc.yaml `
  --only stage0 stage1 stage2 stage3 stage4 stage5

Run Stage 6 only:

python .\analysis_pipeline\run_pipeline.py `
  --config .\analysis_pipeline\config\pipeline_unified_classic_nn_baseline_preproc.yaml `
  --only stage6

When --only stage6 is used through the orchestrator, stage6_confusions is auto-run unless --no-auto-stage6-confusions is passed.

Pipeline summary

Stage Script Main purpose Main outputs
0 build_trial_table.py Build the canonical trial table from BIDS events. <run_root>/reports/trial_table_bids_arithmetic.tsv
1 stage1_qc_summary.py Summarize modality coverage, dropped samples, and participant QC. <run_root>/reports/qc_dataset_summary.json, figures, subject table
2 stage2_preprocess.py Clean EEG, ECG, and pupil streams and write derivatives. <run_root>/derivatives/cleaned/, preprocess logs
3 stage3_epoch_trials.py Convert trials into fixed or overlapping epochs with drop accounting. <run_root>/derivatives/epochs/, epoch_manifest.tsv, epoch_summary.json
4 stage4_extract_features.py Extract modality-specific engineered features. <run_root>/features/features_eeg.tsv, features_ecg.tsv, features_pupil.tsv
5 stage5_build_fused_table.py Build unimodal and fused ML tables plus split manifests. <run_root>/features/features_fused_tutorial_baseline.tsv, split_manifest_tutorial_baseline.json
6 stage6_train_classic_ml.py Benchmark classic and optional deep models across datasets, protocols, and class scenarios. <run_root>/reports/ml_results_*.json, <run_root>/reports/ml_summary_*.md, <run_root>/models/
6b stage6_highlight_confusions.py Curate top confusion matrices from Stage 6 results. <run_root>/reports/confusion_highlights_*.json, markdown, PNGs
6c stage6_build_publication_report.py Assemble a publication-facing run summary. <run_root>/reports/publication_full_report.md, .json

Stage 6 can also emit live confusion PNGs during training and EEG PSD/topomap QC figures when EEG is part of the selected dataset list.

Checked-in profiles

Profile File Intended use
Baseline fixed-window run analysis_pipeline/config/pipeline_unified_classic_nn_baseline_preproc.yaml Canonical reproducible run: fixed 6 s calculation windows, classic plus deep model sweep, publication report enabled.
Overlap-window run analysis_pipeline/config/pipeline_unified_classic_nn_baseline_overlap3s_50pct_preproc.yaml Same pipeline family with 3 s windows, 1.5 s step size, and overlap enabled for Stage 3.

Both profiles benchmark the baseline_all_bins, baseline_omit_easiest, baseline_omit_hardest, baseline_low_high_omit_hardest, and baseline_grouped_4class_omit_hardest class scenarios.

Reproducibility notes

  • run_pipeline.py expands output placeholders such as {reports_dir}, {features_dir}, and {models_dir} from outputs.root.
  • Expected outputs are verified after every step. Use --no-strict-outputs only when debugging incomplete runs.
  • Stage 1 strict QC carry-forward is propagated automatically into Stages 2 to 5.
  • --dry-run prints planned commands and expected outputs without executing them.
  • Stage 6 resolves both Windows and WSL-style dataset paths stored in split manifests.

Documentation map

  • docs/pipeline_reference.md: explicit stage-by-stage and config-by-config pipeline reference.
  • docs/reproducibility.md: artifact policy, rerun guidance, and Linux-to-Windows handoff instructions.
  • analysis_pipeline/README.md: package-level map of stage scripts and outputs.
  • local manuscript handoff material can live under docs/paper_handoff/ without changing the reproducible pipeline entry points.

License

CC0 1.0 Universal (see LICENSE).