Research on extraction and classification of EEG features for multi-class motor imagery

Aiming at solving the problems of low recognition accuracy and poor practicability caused by the small amount of data and insufficient training of the network weights when the deep neural network algorithm is directly used to classify the 4-class motor-imagery electroencephalogram signals(MI-EEG), W...

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Veröffentlicht in:	2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) Jg. 1; S. 693 - 697
Hauptverfasser:	Tang, Xuebin, Zhao, Jinchuang, Fu, Wenli
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.12.2019
Schlagworte:	Brain modeling brain-computer interface systems (BCI) Classification algorithms common spatial pattern (CSP) Convolution convolutional neural network (CNN) Covariance matrices Electroencephalography Feature extraction motor imagery EEG signals (MI-EEG) Training
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