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

  • Image & Signal Processing
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Abstract

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.

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Funding

This project was funded by National Natural Science Foundation of China under the grant 61271352, Natural Science Research Project of Anhui Province (KJ2016A043), Anhui University Center of Information Support & Assurance Technology Open Foundation (ADXXBZ201505).

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Authors and Affiliations

  1. The Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei, China

    Jing Ruan, Xiaopei Wu, Bangyan Zhou, Xiaojing Guo & Zhao Lv

  2. The School of Computer Science and Technology, Anhui University, Hefei, China

    Jing Ruan, Xiaopei Wu, Bangyan Zhou, Xiaojing Guo & Zhao Lv

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  1. Jing Ruan
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Correspondence to Xiaopei Wu.

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Author Jing Ruan declares that she has no conflict of interest. Author Xiaopei Wu declares that he has no conflict of interest. Author Bangyan Zhou declares that she has no conflict of interest. Author Xiaojing Guo declares that she has no conflict of interest. Author Zhao Lv declares that he has no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Ruan, J., Wu, X., Zhou, B. et al. An Automatic Channel Selection Approach for ICA-Based Motor Imagery Brain Computer Interface. J Med Syst 42, 253 (2018). https://doi.org/10.1007/s10916-018-1106-3

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