DSAINet proposes a dual-scale attentive interaction network that outperforms baselines on multiple EEG tasks using the same hyperparameters and only 77K parameters.
UC San Diego Resting State EEG Data from Patients with Parkinson’s Disease
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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.