notebook.ipynb— all code, cells 1–14config.yaml— all hyperparameters, read at startup
Expected total runtime: 5–7 hours.
Two main bottlenecks:
Dataset (~1 hour). The notebook uses Lee2019_ERP via MOABB, which downloads automatically on first run. This is several GB and can take 30–60 minutes depending on your connection. Once downloaded, MOABB caches it locally. The preprocessed epochs are also cached to data/eeg_cache.pkl — if that file exists, the whole loading step is skipped on re-runs.
Evolutionary strategy (~4–6 hours). The ES runs over multiple seeds with up to ea.max_generations generations each. If you just want a quick check, set n_runs: 1 and max_generations: 100 in config.yaml.
numpy, matplotlib, pyyaml, scikit-learn, mne, moabb, pyriemann
Results are saved to results/stage2/checkpoints/ after each seed completes, so a crash mid-run doesn't lose prior work. Cell 11 has commented-out code to reload from checkpoints without re-running the EA.
Everything is seeded from config.yaml. Given the same config and a populated cache, results should be fully reproducible.