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http://dx.doi.org/10.9718/JBER.2011.32.4.295

Accuracy Comparison of Motor Imagery Performance Evaluation Factors Using EEG Based Brain Computer Interface by Neurofeedback Effectiveness  

Choi, Dong-Hag (Department of Electrical & Electronic Engineering, Yonsei University)
Ryu, Yon-Su (Department of Electrical & Electronic Engineering, Yonsei University)
Lee, Young-Bum (Department of Electrical & Electronic Engineering, Yonsei University)
Min, Se-Dong (Department of Electrical & Electronic Engineering, Yonsei University)
Lee, Myoung-Ho (Department of Electrical & Electronic Engineering, Yonsei University)
Publication Information
Journal of Biomedical Engineering Research / v.32, no.4, 2011 , pp. 295-304 More about this Journal
Abstract
In this study, we evaluated the EEG based BCI algorithm using common spatial pattern to find realistic applicability using neurofeedback EEG based BCI algorithm - EEG mode, feature vector calculation, the number of selected channels, 3 types of classifier, window size is evaluated for 10 subjects. The experimental results have been evaluated depending on conditioned experiment whether neurofeedback is used or not In case of using neurofeedback, a few subjects presented exceptional but general tendency presented the performance improvement Through this study, we found a motivation of development for the specific classifier based BCI system and the assessment evaluation system. We proposed a need for an optimized algorithm applicable to the robust motor imagery evaluation system with more useful functionalities.
Keywords
electroencephalography (EEG); brain computer interface (BCI); motor imagery; neurofeedback; common spatial pattern (CSP); EEG mode; feature vector calculation; channel; classifier; window size;
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