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.
Graphsleepnet: Adaptive spatial-temporal graph convolutional networks for sleep stage classification
2 Pith papers cite this work. Polarity classification is still indexing.
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Mamba model reaches 84% balanced accuracy on 3-class sleep staging from multimodal wearable data without EEG in 357 adults with concurrent PSG.
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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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Mamba-based Deep Learning Approach for Sleep Staging on a Wireless Multimodal Wearable System without Electroencephalography
Mamba model reaches 84% balanced accuracy on 3-class sleep staging from multimodal wearable data without EEG in 357 adults with concurrent PSG.