CELM is the first EEG-to-language foundation model that generates clinical reports from variable-length EEG recordings using a new dataset of 9,922 reports paired with 11,000 hours of data from 9,048 patients.
REVE: A foundation model for EEG—adapting to any setup with large-scale pretraining on 25,000 subjects
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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.
Channel adaptation for EEG foundation models is architecture- and regime-dependent, with flexible models showing negative transfer risks during fine-tuning and small models outperforming larger ones on most tasks.
PRiSE-EEG is a prior-guided EEG foundation model that allocates shared and specialized experts across depth using CKA-derived sigmoid mappings and reports strong cross-paradigm results on 12 benchmarks.
citing papers explorer
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Neural Signals Generate Clinical Notes in the Wild
CELM is the first EEG-to-language foundation model that generates clinical reports from variable-length EEG recordings using a new dataset of 9,922 reports paired with 11,000 hours of data from 9,048 patients.
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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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Channel Adaptation for EEG Foundation Models: A Systematic Benchmark Across Architectures, Tasks, and Training Regimes
Channel adaptation for EEG foundation models is architecture- and regime-dependent, with flexible models showing negative transfer risks during fine-tuning and small models outperforming larger ones on most tasks.
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PRiSE-EEG: A Prior-Guided Foundation Model with Depth-Stratified Experts for Cross-Paradigm EEG Representation Learning
PRiSE-EEG is a prior-guided EEG foundation model that allocates shared and specialized experts across depth using CKA-derived sigmoid mappings and reports strong cross-paradigm results on 12 benchmarks.