An Effect-Size Based Channel Selection Algorithm for Mental Task Classification in Brain Computer Interface

The use of large number of channels in EEG based Motor-imagery Brain Computer Interfaces (BCI) may cause long preparation time and redundancy of data. In this paper, we propose a Cohen's d effect-size based channel selection algorithm which eliminates the redundant channels while improving the classification performance. This method (referred to as Effect-size based CSP (E-CSP)) eliminates the channels that do not carry information that distinguishes the two tasks. First, it removes the noisy trials for a channel followed by Cohen's d based effect-size calculation to determine the redundant channels. Using two publicly available BCI competition data sets, the performance of E-CSP algorithm is compared with other existing algorithms like CSP and SCSP. Results indicate that the E-CSP algorithm produces a higher classification accuracy compared to the other algorithms using lesser number of channels in a non-iterative manner.

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