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A deep neural network has been successfully applied to an electroencephalogram (EEG)-based brain-computer interface. However, in most studies, the correlation between EEG channels and inter-region relationships are not well utilized, resulting in sub-optimized spatial feature extraction. In this study, we propose an attention-based dual-stream 3Dconvolutional neural network that can enhance spatial feature extraction by emphasizing the relationship between channels with dot product-based channel attention and 3D convolution. The proposed method showed superior performance than the comparative models by achieving an accuracy of 0.58 for 4-class visual imagery (VI) EEG classification. Through statistical and neurophysiological analysis, visual motion imagery showed higher &#x03B1;-power spectral density (PSD) over the visual cortex than static VI. Also, the VI of swarm dispersion showed higher &#x03B2;-PSD over the pre-frontal cortex than the VI of swarm aggregation.

Decoding 3D Representation of Visual Imagery EEG using Attention-based Dual-Stream Convolutional Neural Network