Optimization of preprocessing stage in EEG based BCI systems in terms of accuracy and timing cost

Abstract

Performance of the motor imagery-based brain computer interface (MI-BCI) systems has been tried to improve by the researchers with novel approaches and methods used on preprocessing stages. In this study, the preprocessing stages are optimized to improve the performance of MI-BCI systems in terms of the accuracy and the timing cost. Taguchi method is adopted for the optimization study. Time window-step size, time interval, theta frequency band, and mu and beta frequency bands are considered as controllable factors in the preprocessing stage. The preprocessing stages are performed according to an experimental plan created by the Taguchi method. The study is applied on BCI Competition IV-2b dataset. The features are extracted from the data with the Hjorth algorithm and classified by using the SVM classifier. The statistical significance and relatively contribution effects of factors on the performance parameters are tested with ANOVA. As a result, optimum combinations of the preprocessing stage factors providing the highest accuracy and the lowest timing cost are both individually and simultaneously revealed. In addition, it is concluded that the most effective factor on the preprocessing stage is determined as time window-step size. Consequently, the results of various combinations of the preprocessing stages on the MI-BCI performance are revealed in terms of both the accuracy and the timing cost.