Correlation Assisted Strong Uncorrelating Transform Complex Common Spatial Patterns for Spatially Distant Channel Data. (15th May 2018)

• Record Type: Journal Article
• Authors: Kim, Youngjoo; You, Jiwoo; Lee, Heejun; Lee, Seung Min; Park, Cheolsoo
• Other Names: Tanaka Toshihisa Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2018(2018)
• Publication Date: 2018-05-15
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2018/4281230
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : 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.