Improving Classification of Multi-class Motor Imagery by Statistical Feature Selection
Publish place: 20th Iranian Student Conference on Electrical Engineering
Publish Year: 1400
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
ISCEE20_029
تاریخ نمایه سازی: 6 مهر 1400
Abstract:
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.
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Authors
Mohammad Dehghan Manshadi
Master student, School of Automotive Engineering, Iran University of Science and Technology, Tehran ۱۶۸۴۶-۱۳۱۱۴, Iran
Abdollah Amirkhani
Assistant professor, School of Automotive Engineering, Iran University of Science and Technology, Tehran ۱۶۸۴۶-۱۳۱۱۴, Iran