Improving Classification of Multi-class Motor Imagery by Statistical Feature Selection

سال انتشار: 1400
محل انتشار: بیستمین کنفرانس ملی دانشجویی مهندسی برق ایران
کد COI اختصاصی: ISCEE20_029
زبان مقاله: انگلیسی
تعداد مشاهده: 398

نویسندگان

Master student, School of Automotive Engineering, Iran University of Science and Technology, Tehran ۱۶۸۴۶-۱۳۱۱۴, Iran

Assistant professor, School of Automotive Engineering, Iran University of Science and Technology, Tehran ۱۶۸۴۶-۱۳۱۱۴, Iran

چکیده

Brain-computer interface (BCI) is a novel technology that is assisting not only disabled people but also healthy people to control an external device by using motor imagery (MI). Although much work has been done in BCI system, achieving ideal accuracy has not been achieved due to the difficulty of pattern recognition of EEG signals. BCI systems are made up of various components that perform preprocessing, feature extraction, and decision making. Common spatial pattern (CSP) is an effective algorithm which is extensively used in extracting feature of EEG motor imagery task. In this article, the CSP algorithm has extended to multi-class classification by one-versus-one (OVO) and one-versus-rest (OVR) methods. To improve classifier in terms of accuracy and less complexity, Fisher algorithm has been used. The average accuracy ۷۳.۴۱ ± ۱.۶۲ has been achieved on BCI Competition IV-IIa dataset. The experimental results show that the Fisher algorithm in reducing complexity and increasing the accuracy of classifier has been effective.

کلیدواژه ها

Brain computer interface, Common spatial pattern, Electroencephalography, Feature selection, Motor imagery task

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