Optimum Feature Selection Using Hybrid Grey Wolf Differential Evolution for Motor Imagery Brain Computer Interface

One of the challenges in improving the performance of brain computer interface systems is to overcome the large number of extracted features from EEG signals. Feature selection can reduce noisy data, overtraining effects, necessary storage, computational complexity, and can improve the performance of the classifier. Different feature selection methods have been used to achieve these goals. In this study, a new hybrid feature selection method is proposed. It employs a filter bank common spatial pattern for feature extraction and a grey wolf optimization algorithm to search and generate optimal feature subset with performance evaluated by support vector machine classifier. Also, In order to increase the search performance of the proposed feature selection algorithm, a new parallel combined grey wolf and differential evolution optimization algorithm is proposed. Experimental results show that the proposed methods improve the performance of motor imagery brain computer interface system in comparison to the state-of-the-art methods, even with small training data.