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
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Zusammenfassung: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 maximizing the mutual information between the spatio-spectral filtering parameters and the class labels. After introducing a nonparametric estimate of mutual information, a gradient-based learning algorithm is devised to efficiently optimize the spatial filters in conjunction with a band-pass filter. The proposed method is compared with two existing methods on real data: a BCI Competition IV dataset as well as our data collected from seven human subjects. The results indicate the superior performance of the method for motor imagery classification, as it produced higher classification accuracy with statistical significance (≥95% confidence level) in most cases.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1045-9227
1941-0093
1941-0093
DOI:10.1109/TNN.2010.2084099