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Abstract To address the challenge of insufficient capture of learnable functional connectivity and discriminative spatial-temporal features in motor imagery EEG (MI-EEG) decoding, this paper proposes a Spatial-Temporal Graph Convolutional Attentional Network (STGCAN-MI). The method first explicitly models the functional connectivity and temporal dependencies among electrodes through a learnable dynamic graph convolution module.
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