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cs.LG2026
Benchmarking Label-Revealed Online Updates for EEG BCI Decoding
Bogdan Kozyrskiy, Artem Grachev, Abraham I. Camelo Guerrero
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 adaptat…
cs.LG2025
EEG-Reptile: An Automatized Reptile-Based Meta-Learning Library for BCIs
Daniil A. Berdyshev, Artem M. Grachev, Sergei L. Shishkin +1
Meta-learning, i.e., "learning to learn", is a promising approach to enable efficient BCI classifier training with limited amounts of data. It can effectively use collections of in…