DSAINet proposes a dual-scale attentive interaction network that outperforms baselines on multiple EEG tasks using the same hyperparameters and only 77K parameters.
BENDR: Using trans- formers and a contrastive self-supervised learning task to learn from massive amounts of EEG data
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EEGM2 is a Mamba-2 integrated self-supervised model for EEG that claims linear complexity and state-of-the-art performance on long-sequence modeling and classification tasks.
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DSAINet: An Efficient Dual-Scale Attentive Interaction Network for General EEG Decoding
DSAINet proposes a dual-scale attentive interaction network that outperforms baselines on multiple EEG tasks using the same hyperparameters and only 77K parameters.
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An Efficient Self-Supervised Framework for Long-Sequence EEG Modeling
EEGM2 is a Mamba-2 integrated self-supervised model for EEG that claims linear complexity and state-of-the-art performance on long-sequence modeling and classification tasks.