Correlation Assisted Strong Uncorrelating Transform Complex Common Spatial Patterns for Spatially Distant Channel Data

作     者：Kim, Youngjoo You, Jiwoo Lee, Heejun Lee, Seung Min Park, Cheolsoo 

作者机构：Kwangwoon Univ Dept Comp Engn Seoul 01897 South Korea Kookmin Univ Sch Elect Engn Coll Creat Engn Seoul 02707 South Korea 

出 版 物：《COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE》 (计算智能与神经科学)

年 卷 期：2018年第2018卷第1期

页      面：1-9页

核心收录：

学科分类：0710[理学-生物学] 1001[医学-基础医学(可授医学、理学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Institute for Information & Communications Technology Promotion (IITP) grant - Korea government (MSIT) [2017-0-00167] Research Grant of Kwangwoon University 

主　　题：MULTICHANNEL communication DATA analysis SPATIAL analysis (Statistics) CORRELATION (Statistics) ELECTROENCEPHALOGRAPHY COMPUTER algorithms 

摘      要：The Strong Uncorrelating Transform Complex Common Spatial Patterns (SUTCCSP) algorithm, designed for multichannel data analysis, has a limitation on keeping the correlation information among channels during the simultaneous diagonalization process of the covariance and pseudocovariance matrices. This paper focuses on the importance of preserving the correlation information among multichannel data and proposes the correlation assisted SUTCCSP (CASUT) algorithm to address this issue. The performance of the proposed algorithm was demonstrated by classifying the motor imagery electroencephalogram (EEG) dataset. The features were first extracted using CSP algorithms including the proposed method, and then the random forest classifier was utilized for the classification. Experiments using CASUT yielded an average classification accuracy of 78.10 (%), which significantly outperformed those of original CSP, Complex Common Spatial Patterns (CCSP), and SUTCCSP with. p-values less than 0.01, tested by the Wilcoxon signed rank test.