
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.


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eegencoder-a-transformer-based-deep-learning-framework-for-robust-motor-imagery-classification-in-brain-computer-interfaces


EEGEncoder: A Transformer-Based Deep Learning Framework for Robust Motor Imagery Classification in Brain-Computer Interfaces


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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.


spatial-temporal-mamba-network-for-enhancing-electroencephalography-based-motor-imagery-classification


Spatial-Temporal Mamba Network for Enhancing Electroencephalography-based Motor Imagery Classification
