A binary harmony search algorithm as channel selection method for motor imagery-based BCI

Background: Channel selection is a key topic in brain-computer interface (BCI). Task-irrelevant and redundant channels used in BCI may lead to low classification accuracy, high computational complexity, and inconvenience for application. By selecting optimal channels, the performance of BCI could enhance significantly. Method: In this paper, a new binary harmony search (BHS) is proposed to select the optimal channel sets and optimize the system accuracy. The BHS is implemented on the training data sets to select the optimal channels and the test data sets are used to evaluate the classification performance on the selected channels. The sparse representation-based classification, linear discriminant analysis, and support vector machine are performed on the common spatial pattern (CSP) features for motor imagery (MI) classification. Results: Two public EEG datasets are employed to validate the proposed BHS method. The paired t-test is conducted on the test classification performance between the BHS and traditional CSP with all channels. The results reveal that the proposed BHS method significantly improved classification accuracy as compared to the conventional CSP method (p < 0.05). Conclusion: This study proposed the BHS method to select the optimal channels in MI-based BCI. On the one hand, the results confirm the validity of the BHS algorithm as a channel selection method for motor imagery data. On the other hand, the BHS method with costing shorter computation time relatively yields a better average test accuracy than the steady-state genetic algorithms. The proposed method could significantly improve the practicability and convenience of the BCI system. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).