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.
Complex brain networks: graph theoretical analysis of structural and functional systems.Nature reviews neuroscience, 10(3):186–198
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RNNs with dynamic constraints applied to EEG data separate brain network activity into three configurations driven by stimuli, tasks, and spontaneous processes, highlighting the parietal network as a central hub.
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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.
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Triple Configuration of Brain Networks Based on Recurrent Neural Networks: The Synergistic Effects of Exogenous Stimuli, Task Demands, and Spontaneous Activity
RNNs with dynamic constraints applied to EEG data separate brain network activity into three configurations driven by stimuli, tasks, and spontaneous processes, highlighting the parietal network as a central hub.