An Automatic Channel Selection Approach for ICA-Based Motor Imagery Brain Computer Interface

• Published in: Journal of medical systems 2018-12, Vol.42 (12), p.253-13, Article 253
• Main Authors: Ruan, Jing, Wu, Xiaopei, Zhou, Bangyan, Guo, Xiaojing, Lv, Zhao
• Format: Article
• Language: English
• Subjects: Algorithms; Brain; Brain-Computer Interfaces; Channels; Classification; Computer applications; Configurations; EEG; Electroencephalography; Feasibility studies; Health Informatics; Health Sciences; Human-computer interface; Humans; Image & Signal Processing; Imagery; Implants; Independent component analysis; Medicine; Medicine & Public Health; Mental task performance; Motors; Optimization; Quality; Quality assessment; Self testing; Signal Processing, Computer-Assisted; Spatial filtering; Statistics for Life Sciences
• ISSN: 0148-5598; 1573-689X
• DOI: 10.1007/s10916-018-1106-3

Independent component analysis (ICA) is a potential spatial filtering method for the implementation of motor imagery brain-computer interface (MIBCI). However, ICA-based MIBCI (ICA-MIBCI) is sensitive to electroencephalogram (EEG) channels and the quality of the training data, which are two crucial factors affecting the stability and classification performance of ICA-MIBCI. To address these problems, this paper is mainly focused on the investigation of EEG channel optimization. As a reference, we constructed a single-trial-based ICA-MIBCI system with commonly used channels and common spatial pattern-based MIBCI (CSP-MIBCI). To minimize the impact of artifacts on EEG channel optimization, a data-quality evaluation method, named “self-testing” in this paper, was used in a single-trial-based ICA-MIBCI system to evaluate the quality of single trials in each dataset; the resulting self-testing accuracies were used for the selection of high-quality trials. Given several candidate channel configurations, ICA filters were calculated using selected high-quality trials and applied to the corresponding ICA-MIBCI implementation. Optimal channels for each dataset were assessed and selected according to the self-testing results related to various candidate configurations. Forty-eight MI datasets of six subjects were employed in this study to validate the proposed methods. Experimental results revealed that the average classification accuracy of the optimal channels yielded a relative increment of 2.8% and 8.5% during self-testing, 14.4% and 9.5% during session-to-session transfer, and 36.2% and 26.7% during subject-to-subject transfer compared to CSP-MIBCI and ICA-MIBCI with fixed the channel configuration. This work indicates that the proposed methods can efficiently improve the practical feasibility of ICA-MIBCI.