EEGEncoder, a novel deep learning framework, effectively combines temporal convolutional networks and transformer models to enhance the classification of motor imagery signals from electroencephalogram (EEG) data, advancing the state-of-the-art in brain-computer interface technology.
STMambaNet, a novel deep learning model, effectively captures the intricate spatial-temporal dynamics in electroencephalography (EEG) signals to significantly improve the decoding performance of motor imagery (MI) classification.