Title: Some considerations of common spatial pattern for better classification in brain-computer interfaces
Authors: June-Hyoung Kim; Yeon-Mo Yang
Addresses: School of Electronic Engineering, Kumoh National Institute of Technology, Gumi, 39177, South Korea ' School of Electronic Engineering, Kumoh National Institute of Technology, Gumi, 39177, South Korea
Abstract: EEG-based motor imagery signal classification is very important in brain-computer interface (BCI) technology. In this work, we develop a common spatial pattern (CSP) technique for feature extraction in a BCI system. To confirm classification improvement, classification accuracy was analysed by using four statistics, namely mean, variance, skewness, and kurtosis within the CSP paradigm. The data from the dataset III of BCI competition II were used and simulated using MATLAB. The results show that the best classification accuracy is obtained when the CSP algorithm uses the variance statistic for feature extraction.
Keywords: brain-computer interface; BCI; motor imagery; common spatial pattern; CSP; neural signal classification; statistical signal processing.
International Journal of Telemedicine and Clinical Practices, 2018 Vol.3 No.1, pp.32 - 43
Received: 21 Mar 2017
Accepted: 11 Jan 2018
Published online: 16 Jul 2018 *