A Novel Deep Learning Scheme for Motor Imagery EEG Decoding Based on Spatial Representation Fusion

Motor imagery electroencephalography (MI-EEG), which is an important subfield of active brain-computer interface (BCI) systems, can be applied to help disabled people to consciously and directly control prosthesis or external devices, aiding them in certain daily activities. However, the low signal-...

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Veröffentlicht in:	IEEE access Jg. 8; S. 202100 - 202110
Hauptverfasser:	Yang, Jun, Ma, Zhengmin, Wang, Jin, Fu, Yunfa
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Artificial neural networks Biological neural networks Brain–computer interface Convolution convolution neural network Datasets Decoding Deep learning discriminative and representative deep learning Electroencephalography feature fusion Human-computer interface Imagery Multilayers People with disabilities Prostheses Representations Signal to noise ratio Spatial resolution stacked sparse autoencoder Task analysis Training
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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