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.
citing papers explorer
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The Identity Trap in EEG Foundation Models: A Diagnostic Audit
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.
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Aperiodic and Low-Frequency Spectral Bias in Reconstruction based EEG Foundation Models
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.
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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.
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Modality vs. Morphology: A Framework for Time Series Classification for Biological Signals
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.