M-FANet: Multi-Feature Attention Convolutional Neural Network for Motor Imagery Decoding

M-FANet: Multi-Feature Attention Convolutional Neural Network for Motor Imagery Decoding

Motor imagery (MI) decoding methods are pivotal in advancing rehabilitation and motor control research. Effective extraction of spectral-spatial-temporal features is crucial for MI decoding from limited and low signal-to-noise ratio electroencephalogram (EEG) signal samples based on brain-computer i...

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Detalles Bibliográficos
Main Authors: Yiyang Qin, Banghua Yang, Sixiong Ke, Peng Liu, Fenqi Rong, Xinxing Xia
Formato: Artigo
Idioma:English
Publicado: IEEE 2024-01-01
Series:IEEE Transactions on Neural Systems and Rehabilitation Engineering
Subjects:
Brain–computer interface
motor imagery
convolutional neural networks
multi-feature attention
Acceso en liña:https://ieeexplore.ieee.org/document/10385159/

Internet

https://ieeexplore.ieee.org/document/10385159/

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