arXiv AI
PHASE: A Physiology-Guided Hierarchical Foundation Model for Intracranial EEG
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Neural State Prediction: Obstructing Shortcut Learning in EEG Foundation Models
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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 AI
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Taming foundation model with invariance-oriented pre-training for broad-spectrum EEG analysis across signal-level, brain-state, and brain-health tasks
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arXiv AI
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Multimodal Pretraining for Generalizable EEG Representation Learning
arXiv:2607. 21384v1 Announce Type: new Abstract: Electroencephalography (EEG) models used for epilepsy are often limited to specific datasets and tasks.
arXiv AI
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A foundation model with multi-variate parallel attention to generate neuronal activity
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arXiv AI
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EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding
arXiv:2608. 13072v1 Announce Type: new Abstract: Electroencephalography (EEG) decoding models often generalize poorly across datasets and subjects due to domain shifts in acquisition protocols and individual neurophysiology.