No abstract

Brain-computer interface (BCI) provides a new communication channel between human brain and computer. In order to eliminate uncorrelated channels to improve BCI performance and enhance user convenience with fewer channels, this paper proposes a new framework using binary adaptive differential evolution bat algorithm (BADEBA). The framework uses the important ideas of differential evolution algorithm and bat algorithm to select electroencephalograph (EEG) channels and intelligently optimizes the parameters of support vector machine (SVM). It combines wavelet packet transform (WPT) and common space pattern (CSP) to achieve the goal of using fewer channels to obtain the best classification accuracy. The proposed framework is evaluated with a common data set (DEAP). The results show that, compared with genetic algorithm (GA), binary particle swarm optimization (BPSO) and bat algorithm, the proposed BADEBA in this framework only uses eight channels to improve the classification accuracy by 13.63% in the valence dimension and seven channels to improve the classification accuracy by 15.22% in the arousal dimension. In addition, the spatial distribution of the best channels selected by this method is consistent with the existing knowledge of brain structure and neurophysiology, which shows the accuracy and validity of this method.