Bayesian Complete-Pooling in Cross-Subject Classification for Motor Imagery Electroencephalogram
Brain-computer interfaces (BCIs) have long sought calibration-free operation, yet classifiers are typically benchmarked on discrimination alone. Discrimination is blind to calibration, a meaningful gap given that electroencephalogram (EEG) signals are nonstationary and point-estimate classifiers can become overconfident under distribution shift. We conducted a large-scale study contrasting Bayesian complete-pooling models against frequentist baselines for cross-subject, left-hand versus right-hand motor imagery EEG classification across 20 datasets. Each of six frequentist pipelines was paired with an analogous Bayesian pipeline sharing identical feature engineering, fit via Markov chain ...
| Authors | Ethan Davis |
|---|---|
| Primary category | cs.LG |
| Published | 25 July 2026 |
| Last updated at source | 25 September 2026 |
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