Abstract
Classifying single-trial electroencephalogram (EEG) based motor imagery (MI) tasks is extensively used to control brain-computer interface (BCI) applications, as a communication bridge between humans and computers. However, the low signal-to-noise ratio and individual differences of EEG can affect the classification results negatively. In this paper, we propose an improved common spatial pattern (B-CSP) method to extract features for alleviating these adverse effects. First, for different subjects, the method of Bhattacharyya distance is used to select the optimal frequency band of each electrode including strong event-related desynchronization (ERD) and event-related synchronization (ERS) patterns; then the signals of the optimal frequency band are decomposed into spatial patterns, and the features that can describe the maximum differences of two classes of MI are extracted from the EEG data. The proposed method is applied to the public data set and experimental data set to extract features which are input into a back propagation neural network (BPNN) classifier to classify single-trial MI EEG. Another two conventional feature extraction methods, original common spatial pattern (CSP) and autoregressive (AR), are used for comparison. An improved classification performance for both data sets (public data set: 91.25%±1.77% for left hand vs. foot and 84.50%±5.42% for left hand vs. right hand; experimental data set: 90.43%±4.26% for left hand vs. foot) verifies the advantages of the B-CSP method over conventional methods. The results demonstrate that our proposed B-CSP method can classify EEG-based MI tasks effectively, and this study provides practical and theoretical approaches to BCI applications.
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Zhi-chuan TANG, Chao LI, Jian-feng WU, Peng-cheng LIU, and Shi-wei CHENG declare that they have no conflict of interest.
The Ethics Committee of Zhejiang University of Technology had reviewed the experimental procedure and method, and approved this experiment. Before the experiment, all subjects signed the informed written consent and agreed to participate in this experiment.
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Project supported by the National Natural Science Foundation of China (Nos. 61702454 and 61772468), the MOE Project of Humanities and Social Sciences, China (No. 17YJC870018), the Fundamental Research Funds for the Provincial Universities of Zhejiang Province, China (No. GB201901006), and the Philosophy and Social Science Planning Fund Project of Zhejiang Province, China (No. 20NDQN260YB)
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Tang, Zc., Li, C., Wu, Jf. et al. Classification of EEG-based single-trial motor imagery tasks using a B-CSP method for BCI. Frontiers Inf Technol Electronic Eng 20, 1087–1098 (2019). https://doi.org/10.1631/FITEE.1800083
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DOI: https://doi.org/10.1631/FITEE.1800083
Key words
- Electroencephalogram (EEG)
- Motor imagery (MI)
- Improved common spatial pattern (B-CSP)
- Feature extraction
- Classification