Genetic-based feature selection for efficient motion imaging of a brain–computer interface framework

Abstract

Objective. A brain–computer interface (BCI) equips humans with the ability to control computers and technical devices mentally. However, the enormous data and the existing irrelevant features of the electrocorticogram signal limit the performance of the classifier. To address these problems, a novel signal processing framework for a binary motor imagery-based BCI system (MI–BCI) is proposed in this paper. Approach. Stockwell transform and Bayesian linear discriminant analysis were applied to feature extraction and classification, respectively, and a genetic algorithm (GA) was used in the process of feature selection to extract the most relevant features for classification. The superiority of the algorithm is demonstrated through test results based on the BCI Competition III dataset I. Main results. By comparing the processes with or without feature selection, the performance of the classification was proven to improve using the GA. By adjusting the parameters of the GA, the best feature set (selected 48.6% features) was selected to achieve classification sensitivity, specificity, precision, and accuracy of 94%, 98%, 97.9%, and 96%, respectively, exceeding the results of the existing state-of-the art algorithms. Significance. As the proposed method can reduce the number of features and select the best feature set, its classification performance was improved and the classification time was shortened; thus, it can be applied to various BCI systems.