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http://dx.doi.org/10.5573/ieie.2014.51.5.197

Filter Selection Method Using CSP and LDA for Filter-bank based BCI Systems  

Park, Geun-Ho (Department of Electrical and Computer Eng., Pusan National University)
Lee, Yu-Ri (Department of Electrical and Computer Eng., Pusan National University)
Kim, Hyoung-Nam (Department of Electrical and Computer Eng., Pusan National University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.51, no.5, 2014 , pp. 197-206 More about this Journal
Abstract
Motor imagery based Brain-computer Interface(BCI), which has recently attracted attention, is the technique for decoding the user's voluntary motor intention using Electroencephalography(EEG). For classifying the motor imagery, event-related desynchronization(ERD), which is the phenomenon of EEG voltage drop at sensorimotor area in ${\mu}$-band(8-13Hz), has been generally used but this method are not free from the performance degradation of the BCI system because EEG has low spatial resolution and shows different ERD-appearing band according to users. Common spatial pattern(CSP) was proposed to solve the low spatial resolution problem but it has a disadvantage of being very sensitive to frequency-band selection. Discriminative filter bank common spatial pattern(DFBCSP) tried to solve the frequency-band selection problem by using the Fisher ratio of the averaged EEG signal power and establishing discriminative filter bank(DFB) which only includes the feature frequency-band. However, we found that DFB might not include the proper filters showing the spatial pattern of ERD. To solve this problem, we apply a band-selection process using CSP feature vectors and linear discriminant analysis to DFBCSP instead of the averaged EEG signal power. The filter selection results and the classification accuracies of the existing and the proposed methods show that the CSP feature is more effective than signal power feature.
Keywords
EEG; common spatial pattern;
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