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洞見 - EEG-based Motor Imagery Classification
EEGEncoder: A Transformer-Based Deep Learning Framework for Robust Motor Imagery Classification in Brain-Computer Interfaces
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.
Spatial-Temporal Mamba Network for Enhancing Electroencephalography-based Motor Imagery Classification
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.
