Combining Spatial Filters for the Classification of Single-Trial EEG in a Finger Movement Task

Brain-computer interface (BCI) is to provide a communication channel that translates human intention reflected by a brain signal such as electroencephalogram (EEG) into a control signal for an output device. In recent years, the event-related desynchronization (ERD) and movement-related potentials (...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2007-05, Vol.54 (5), p.821-831
Main Authors: Liao, Xiang, Yao, Dezhong, Wu, Dan, Li, Chaoyi
Format: Article
Language:English
Subjects:
Algorithms
Brain computer interfaces
Brain Mapping
Brain-computer interface (BCI)
common spatial patterns (CSP)
Communication channels
Digital signal processing
discriminative spatial patterns (DSP)
Electroencephalography
Electroencephalography - methods
event-related desynchronization (ERD)
Evoked Potentials, Motor
Fingers
Fingers - physiology
Humans
Materials requirements planning
Microelectrodes
Movement - physiology
movement related potentials (MRPs)
Pattern Recognition, Physiological
Spatial filters
Support vector machine classification
Support vector machines
Time Factors
User-Computer Interface
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Summary:Brain-computer interface (BCI) is to provide a communication channel that translates human intention reflected by a brain signal such as electroencephalogram (EEG) into a control signal for an output device. In recent years, the event-related desynchronization (ERD) and movement-related potentials (MRPs) are utilized as important features in motor related BCI system, and the common spatial patterns (CSP) algorithm has shown to be very useful for ERD-based classification. However, as MRPs are slow nonoscillatory EEG potential shifts, CSP is not an appropriate approach for MRPs-based classification. Here, another spatial filtering algorithm, discriminative spatial patterns (DSP), is newly introduced for better extraction of the difference in the amplitudes of MRPs, and it is integrated with CSP to extract the features from the EEG signals recorded during voluntary left versus right finger movement tasks. A support vector machines (SVM) based framework is designed as the classifier for the features. The results show that, for MRPs and ERD features, the combined spatial filters can realize the single-trial EEG classification better than anyone of DSP and CSP alone does. Thus, we propose an EEG-based BCI system with the two feature sets, one based on CSP (ERD) and the other based on DSP (MRPs), classified by SVM
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2006.889206