Selection of Relevant Electrodes Based on Temporal Similarity for Classification of Motor Imagery Tasks

Selection of relevant electrodes is of prime importance for developing efficient motor imagery Brain Computer Interface devices. In this paper, we propose a novel spectral clustering based on temporal similarity of electrodes to select a reduced set of relevant electrodes for classification of motor imagery tasks. Further, Stationary common spatial pattern method in conjunction with Composite kernel Support Vector Machine is utilized to develop a decision model. Experimental results demonstrate improvement in classification accuracy in comparison to variants of the common spatial pattern method on publicly available datasets. Friedman statistical test shows that the proposed method significantly outperformed the variants of the common spatial pattern method.