Improvement Motor Imagery EEG Classification Based on Regularized Linear Discriminant Analysis
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Mental tasks classification such as motor imagery, based on EEG signals is an important problem in brain computer interface systems (BCI). One of the major concerns in BCI is to have a high classification accuracy. The other concerning one is with the favorable result is guaranteed how to improve the computational efficiency. In this paper, Mu/Beta rhythm was obtained by bandpass filter from EEG signal. And the classical linear discriminant analysis (LDA) was used for deciding which rhythm can give the better classification performance. During this, the common spatial pattern (CSP) was used to project data subject to the ratio of projected energy of one class to that of the other class was maximized. The optimal projection dimension was determined corresponding to the maximum of area under the curve (AUC) for each participant. Eventually, regularized linear discriminant analysis (RLDA) is possible to decode the imagined motor sensed using electroencephalogram (EEG). Results show that higher classification accuracy can be provided by RLDA. And optimal projection dimensions determined by LDA and RLDA are of consistent solution, this improves computational efficiency of CSP-RLDA method without computation of projection dimension.
KeywordsElectroencephalogram classification Common spatial pattern Regularized linear discriminant analysis
This work was supported by the National Natural Science Foundation of China (Grant No. 51605419, 51475407), Natural Science Foundation of Hebei Province (Grant No. E2018203433), China Postdoctoral Science Foundation (Grant No. 2016 M600193), Hebei Province Funding Project for Returned Overseas Scholar (Grant No. CL201727).
National Natural Science Foundation of China (Grant No. 51605419, 51475407), Natural Science Foundation of Hebei Province (Grant No. E2018203433), China Postdoctoral Science Foundation (Grant No. 2016 M600193), Hebei Province Funding Project for Returned Overseas Scholar (Grant No. CL201727).
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Conflict of Interest
The authors declare that they have no conflict of interest.
Clarification and Statement
This manuscript by Rongrong Fu, Yongsheng Tian, Tiantian Bao, Zong Meng, Peiming Shi titled “Improvement Motor Imagery EEG Classification based on Regularized Linear Discriminant Analysis” is an original unpublished work and the manuscript or any variation of it has not been submitted to any other publication previously. All of the authors have agreed with the submission.
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