aceThe challenge of out-of-distribution detection in motor imagery BCIs
Objective . Machine learning classifiers used in Brain-Computer Interfaces (BCIs) make classifications based on the distribution of examples on which they were trained. When exposed to electroencephalography (EEG) from unfamiliar classes they can only make blind guesses. Instead of allowing such guesses, these out-of-distribution (OOD) samples should be detected and rejected to improve the robustness of BCIs against unfamiliar cognitive states. Approach . We study OOD detection in Motor Imagery BCIs by training…
For subjects and models with high task performance, Deep Ensemble and Monte Carlo-Dropout classifiers reached OOD detection scores up to 0.7, making rejection of unfamiliar cognitive states feasible.
Because EEG is highly variable, uncertainty for familiar classes can exceed that for unfamiliar ones, so many OOD detection methods that work in other machine learning domains fail on motor imagery BCIs.
OOD detection ability reached only up to 0.7 and was effective mainly for models and subjects with high task performance, while many methods were ineffective for many BCI users.
Evidence
- Peer-reviewedJournal of Neural Engineering2026-10-09
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Truvace Impact Record TRV-2026-1609, v1: “The challenge of out-of-distribution detection in motor imagery BCIs.” Truvace, 2026-10-16. /record/TRV-2026-1609 (accessed at citation time). sha256 e74c433676fb80c1…
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