Benchmarking Label-Revealed Online Updates for EEG BCI Decoding

Abstract:Electroencephalography (EEG) signals drift over time, which can cause static brain-computer interface (BCI) models to degrade in practice. We present a benchmark for online adaptation and compare two widely used pipeline families, Common Spatial Patterns (CSP) and Riemannian covariance-based methods, under time-ordered prequential (test-then-train) evaluation. We examine (i) which pipelines benefit most from label-revealed updates, (ii) whether controlled forgetting of older data improves robustness, and (iii) how a minimal-calibration cold start compares with starting from a pretrained model. Across four datasets (three motor-imagery datasets and one movement-decoding dataset), label-revealed online updates improve 13 of 14 model/dataset pairs on the two largest streams, with relative accuracy gains of up to about 18% over a frozen model. A Shapley-based data-valuation analysis over temporal blocks assigns the largest mean value to the most recent block in each of the three analyzed datasets, while older blocks retain positive value.

Machine Learning (cs.LG); Signal Processing (eess.SP)

arXiv:2610.07420 [cs.LG]

(or arXiv:2610.07420v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.07420 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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