Overnight Proposals - 2026-07-03#
Recon Sources#
Consensus: EEG foundation model benchmarking remains unsettled; larger models do not reliably improve cross-subject generalization, and specialist baselines remain competitive: EEG Foundation Models: Progresses, Benchmarking, and Open Problems.
Consensus: segment/window leakage can strongly inflate translational EEG results: Data leakage in deep learning studies of translational EEG.
Consensus: log-loss forecastability across horizons is an information-theoretic object, not just a model leaderboard: Forecastability as an Information-Theoretic Limit on Prediction.
Web: official data sources are CHB-MIT and Sleep-EDF on PhysioNet.
opensrc: checked
scikit-learn/scikit-learnlogistic source andscipy/scipyoptimizer source. Decision: do not add SciPy because this repo does not depend on it; use a small IRLS offset-GLM.
Candidate Scores#
candidate |
novelty |
feasible tonight |
impact if true |
leakage risk |
decision |
|---|---|---|---|---|---|
Leakage-safe forecastability-vs-horizon curve |
4 |
5 |
4 |
2 |
chosen |
Nuisance-invariant residual representation with probe gate |
4 |
3 |
4 |
3 |
defer |
Cross-modal missing-modality recoverability/identifiability gate |
5 |
2 |
5 |
3 |
defer |
Chosen Candidate#
Build M4: a forecastability-vs-horizon information curve. Labels are shifted within each patient only, then RFS is recomputed per horizon against the best nuisance/trivial baseline. This directly extends the existing RFS ladder and gives the repo a compact benchmark-method contribution without adding a new model family.
Why The Other Two Wait#
Nuisance-invariant representation learning is useful, but the current M3 artifact already shows the cheaper issue: the model must first beat moving-average/history baselines. Cross-modal recoverability is closer to the broad NeuroTwin v1 claim, but needs paired multimodal real data and stronger baseline coverage than this workspace can honestly create overnight.