CoCoT-EEG: Contrastive-Pretrained Multiscale Convolutional Transformer for EEG Decoding
Self-supervised pretrained foundation models (FM) have shown early promise for non-invasive electroencephalogram (EEG) decoding applications. Many recent large-scale models converged on the approach of tokenizing raw EEG followed by masked reconstruction pretraining. However, this recipe has been shown to be suboptimal for data, like EEG, with high noise amplitude and information confined to limited dimensions such as narrow frequency bands. Building on this insight, we develop a novel contrastive-pretrained EEG model with multiscale temporal convolution input layers and Transformer encoder bl
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- PossiblePossibly related (embedding) · 48%eegdash/EEGDash →
- LinkedLinked via arxiv author · 85%Gabriel Mahuas →
“CoCoT-EEG: Contrastive-Pretrained Multiscale Convolutional Transformer for EEG Decoding”
- LinkedLinked via arxiv author · 85%Victoria Shevchenko →
“CoCoT-EEG: Contrastive-Pretrained Multiscale Convolutional Transformer for EEG Decoding”
- LinkedLinked via arxiv author · 85%Ugo Tanielian →
“CoCoT-EEG: Contrastive-Pretrained Multiscale Convolutional Transformer for EEG Decoding”
- LinkedLinked via arxiv author · 85%Yassir Bendou →
“CoCoT-EEG: Contrastive-Pretrained Multiscale Convolutional Transformer for EEG Decoding”
- LinkedLinked via arxiv author · 85%Richard Gao →
“CoCoT-EEG: Contrastive-Pretrained Multiscale Convolutional Transformer for EEG Decoding”