Subject identity variance dominates frozen representations in three EEG foundation models by 13-89x over null, and erasing the linear subject axis improves label decoding where within-subject label variation exists.
Bci2000: A general-purpose brain-computer interface (bci) system
4 Pith papers cite this work. Polarity classification is still indexing.
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Reconstruction-based EEG foundation models preferentially encode aperiodic and low-frequency components over oscillatory structure, with embeddings capturing subject identity more than task-relevant information.
Controlled comparison finds that a pretrained time-series foundation model can be effectively used as a frozen temporal feature extractor in EEG foundation models, with task-specific performance differences.
A review synthesizes evidence from EEG, EMG, ECG, PPG and ocular signals to argue that waveform morphology, rather than modality or model class, primarily determines TSC performance and interpretability.
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Temporal Feature Extractors in EEG Foundation Models: A Controlled Comparison Including a Pretrained Time-Series Model
Controlled comparison finds that a pretrained time-series foundation model can be effectively used as a frozen temporal feature extractor in EEG foundation models, with task-specific performance differences.