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Computational Intelligence and Neuroscience
Volume 2018, Article ID 4281230, 9 pages
Research Article

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

1Department of Computer Engineering, Kwangwoon University, Seoul 01897, Republic of Korea
2School of Electrical Engineering, College of Creative Engineering, Kookmin University, Seoul 02707, Republic of Korea

Correspondence should be addressed to Cheolsoo Park;

Youngjoo Kim and Jiwoo You contributed equally to this work.

Received 28 September 2017; Revised 26 February 2018; Accepted 1 April 2018; Published 15 May 2018

Academic Editor: Toshihisa Tanaka

Copyright © 2018 Youngjoo Kim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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 -values less than 0.01, tested by the Wilcoxon signed rank test.