EEG-MFTNet enhances EEGNet with multi-scale temporal convolutions and transformer fusion to achieve 58.9% average accuracy in subject-dependent cross-session motor imagery classification on the SHU dataset while keeping low computational cost.
An in-depth survey on deep learning-based motor imagery electroencephalogram (eeg) classification
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EEG-MFTNet: An Enhanced EEGNet Architecture with Multi-Scale Temporal Convolutions and Transformer Fusion for Cross-Session Motor Imagery Decoding
EEG-MFTNet enhances EEGNet with multi-scale temporal convolutions and transformer fusion to achieve 58.9% average accuracy in subject-dependent cross-session motor imagery classification on the SHU dataset while keeping low computational cost.