Optimum Spatio-Spectral Filtering Network for Brain-Computer Interface

This paper proposes a feature extraction method for motor imagery brain-computer interface (BCI) using electroencephalogram. We consider the primary neurophysiologic phenomenon of motor imagery, termed event-related desynchronization, and formulate the learning task for feature extraction as maximiz...

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Veröffentlicht in:	IEEE transactions on neural networks Jg. 22; H. 1; S. 52 - 63
Hauptverfasser:	Zhang, Haihong, Chin, Zheng Yang, Ang, Kai Keng, Guan, Cuntai, Wang, Chuanchu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.01.2011 Institute of Electrical and Electronics Engineers
Schlagworte:	Applied sciences Artificial intelligence Band pass filters Brain - physiology Brain-computer interface Computer science; control theory; systems Computer systems and distributed systems. User interface Electroencephalography Electroencephalography - methods Entropy Evoked Potentials, Motor - physiology Exact sciences and technology Feature extraction Humans Male motor imagery electroencephalography Mutual information Neural Networks (Computer) Optimization Pattern Recognition, Automated - standards Rhythm Software spatio-spectral filtering User-Computer Interface Brain Gradient Statistical analysis Filtering Electroencephalography Pattern recognition Network interfaces spatio-spectral filtering motor imagery electroencephalography Optimization Search algorithm Brain-computer interface User interface Classification Feature extraction Spatial filters Desynchronization Learning algorithm Artificial intelligence Pattern extraction Mutual information
ISSN:	1045-9227, 1941-0093, 1941-0093
Online-Zugang:	Volltext
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