A Multi-Domain Convolutional Neural Network for EEG-Based Motor Imagery Decoding

Motor imagery (MI) decoding plays a crucial role in the advancement of electroencephalography (EEG)-based brain-computer interface (BCI) technology. Currently, most researches focus on complex deep learning structures for MI decoding. The growing complexity of networks may result in overfitting and...

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Veröffentlicht in:	IEEE transactions on neural systems and rehabilitation engineering 2023, Vol.31, p.3988-3998
Hauptverfasser:	Zhi, Hongyi, Yu, Zhuliang, Yu, Tianyou, Gu, Zhenghui, Yang, Jian
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Brain Brain–computer interface (BCI) center loss Complexity Computer applications Convolution convolutional neural network (CNN) Datasets Decoding Deep learning EEG Electroencephalography electroencephalography (EEG) Feature extraction Filter banks Frequency dependence Human-computer interface Imagery Implants Kernel Machine learning Mental task performance motor imagery (MI) Neural networks Temporal variations Time-frequency analysis
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