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Learning Advanced Brain Computer Interface Technology: Comparing CSP Algorithm and WPA Algorithm for EEG Feature Extraction

Learning Advanced Brain Computer Interface Technology: Comparing CSP Algorithm and WPA Algorithm for EEG Feature Extraction

Wang Tao, Wu Linyan, Li Yanping, Gao Nuo, Zhang Weiran
Copyright: © 2019 |Volume: 15 |Issue: 3 |Pages: 14
ISSN: 1548-3908|EISSN: 1548-3916|EISBN13: 9781522564164|DOI: 10.4018/IJTHI.2019070102
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MLA

Tao, Wang, et al. "Learning Advanced Brain Computer Interface Technology: Comparing CSP Algorithm and WPA Algorithm for EEG Feature Extraction." IJTHI vol.15, no.3 2019: pp.14-27. http://doi.org/10.4018/IJTHI.2019070102

APA

Tao, W., Linyan, W., Yanping, L., Nuo, G., & Weiran, Z. (2019). Learning Advanced Brain Computer Interface Technology: Comparing CSP Algorithm and WPA Algorithm for EEG Feature Extraction. International Journal of Technology and Human Interaction (IJTHI), 15(3), 14-27. http://doi.org/10.4018/IJTHI.2019070102

Chicago

Tao, Wang, et al. "Learning Advanced Brain Computer Interface Technology: Comparing CSP Algorithm and WPA Algorithm for EEG Feature Extraction," International Journal of Technology and Human Interaction (IJTHI) 15, no.3: 14-27. http://doi.org/10.4018/IJTHI.2019070102

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Abstract

Feature extraction is an important step in electroencephalogram (EEG) processing of motor imagery, and the feature extraction of EEG directly affects the final classification results. Through the analysis of various feature extraction methods, this article finally selects Common Spatial Patterns (CSP) and wavelet packet analysis (WPA) to extract the feature and uses Support Vector Machine (SVM) to classify and compare these extracted features. For the EEG data provided by GRAZ University, the accuracy rate of feature extraction using CSP algorithm is 85.5%, and the accuracy rate of feature extraction using wavelet packet analysis is 92%. Then this paper analyzes the EEG data collected by Emotiv epoc+ system. The classification accuracy of wavelet packet extracted features can still be maintained at more than 80%, while the classification accuracy of CSP extracted feature is decreased obviously. Experimental results show that the method of wavelet packet analysis towards competition data and Emotiv epoc+ system data can both get a desirable outcome.

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