EEG foundation models are outperformed by task-specific models on a new rigorous 4-letter handwriting decoding task from EEG, with performance dropping without movement-onset knowledge and improving more from better test-time signals than from scaling data.
MIRepNet: A pipeline and foundation model for EEG-based motor imagery classification
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NeuroAtlas benchmarks foundation models on 42 EEG datasets and reports that EEG-specific models do not consistently outperform generic time-series models, standard metrics miss clinical utility, and rankings vary by domain.
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Handwriting decoding as a challenging motor task for EEG Foundation Models
EEG foundation models are outperformed by task-specific models on a new rigorous 4-letter handwriting decoding task from EEG, with performance dropping without movement-onset knowledge and improving more from better test-time signals than from scaling data.
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NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces
NeuroAtlas benchmarks foundation models on 42 EEG datasets and reports that EEG-specific models do not consistently outperform generic time-series models, standard metrics miss clinical utility, and rankings vary by domain.