In order to perform the channel selection, we applied Corona proposed in [1]. Each of EEG signals was transformed into a vector using either the upper triangle elements of its correlation coefficient matrix or the upper diagonal elements of its covariance matrix including the diagonal elements. Both the max and mean aggregating functions have been utilized. As a classifier, Support Vector Machine (SVM) with linear kernel has been employed both for the channel selection and the cross validation. For each subject, the training and testing have been separately performed, not utilizing the data from other subjects. Subject FSS_method # of selected channels CV accuracy aa Corona (mean, cov) 47 64.4% al Corona (mean, corr) 65 79.0% av Corona (max, corr) 32 72.0% aw Corona (mean, cov) 21 91.0% ay Corona (mean, cov) 21 80.3% [1] Kiyoung Yang, Hyunjin Yoon, Cyrus Shahabi, A Supervised Feature Subset Selection Technique for Multivariate Time Series, International Workshop on Feature Selection for Data Mining: Interfacing Machine Learning with Statistics (FSDM) in conjunction with 2005 SIAM International Conference on Data Mining (SDM'05), Newport Beach, CA, April 2005