NeuroAtlas benchmarks foundation models on 42 EEG datasets and reports that EEG-specific models do not consistently outperform generic time-series models, standard metrics miss clinical utility, and rankings vary by domain.
The temple university hospital eeg data corpus.Frontiers in neuroscience, 10:196
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CLEF, a long-context EEG foundation model using 3D multitaper spectrograms and contrastive alignment with reports and EHR, beats prior models on 229 of 234 clinical tasks and raises mean AUROC from 0.65 to 0.74.
CodeBrain introduces a decoupled TFDual-Tokenizer and multi-scale EEGSSM architecture for an EEG foundation model pretrained on a large corpus, claiming strong generalization across eight downstream tasks and ten datasets.
citing papers explorer
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NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces
NeuroAtlas benchmarks foundation models on 42 EEG datasets and reports that EEG-specific models do not consistently outperform generic time-series models, standard metrics miss clinical utility, and rankings vary by domain.
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CLEF: EEG Foundation Model for Learning Clinical Semantics
CLEF, a long-context EEG foundation model using 3D multitaper spectrograms and contrastive alignment with reports and EHR, beats prior models on 229 of 234 clinical tasks and raises mean AUROC from 0.65 to 0.74.
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CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model
CodeBrain introduces a decoupled TFDual-Tokenizer and multi-scale EEGSSM architecture for an EEG foundation model pretrained on a large corpus, claiming strong generalization across eight downstream tasks and ten datasets.