arXiv Machine Learning
Behavioral Latency as Weak Event-Time Supervision for EEG Reaction-Time Decoding
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Neural State Prediction: Obstructing Shortcut Learning in EEG Foundation Models
Neural State Prediction (NSP) is a latent‑predictive framework designed to curb shortcut learning in EEG foundation models. By using a target encoder updated with an exponential moving average, identity residualization, and topology‑separated context, NSP constrains both the prediction target and the available context. Trained on 2.2 million EEG segments, NSP outperforms baselines on 14 datasets in the EEG‑FM‑Bench, achieving 63.94 % macro balanced accuracy.
arXiv AI
Jul 7
Test-Time Adaptation for EEG Foundation Models: A Systematic Study under Real-World Distribution Shifts
arXiv:2604. 16926v2 Announce Type: replace-cross Abstract: Electroencephalography (EEG) foundation models have shown strong potential for learning generalizable representations from large-scale neural data, yet their clinical deployment is hindered by distribution shifts across clinical settings, devices, and populations.
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PHASE: A Physiology-Guided Hierarchical Foundation Model for Intracranial EEG
arXiv:2609.36087v1 Announce Type: cross Abstract: Clinicians and neuroscientists have long analyzed intracranial electroencephalography (iEEG) through directly measurable physiological characteristic...
arXiv Machine Learning
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EEG-PRISM: Physiologically-Grounded Interpretability of Predictions by EEG Foundation Models
arXiv:2608. 13676v1 Announce Type: new Abstract: Objective: Foundation models represent the next advancement in AI for EEG analysis; however current explainable AI techniques provide attribution scores in the time-channel input space, which is mismatched to clinical intuition about EEG.
arXiv Machine Learning
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Learning Dynamic Neural Evidence Representations for Time-Adaptive Brain-Computer Interfaces
arXiv:2609.22088v1 Announce Type: cross Abstract: Brain-computer interfaces (BCIs) decode neural activity into commands, yet most existing systems rely on fixed-window decoding that may result in red...
arXiv AI
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Boundary Density Likelihood for Direct Event-Time Supervision
arXiv:2408. 12792v2 Announce Type: replace Abstract: Event detection turns long recordings into a sparse set of ranked timestamps.