Correlation and Sparse Representation Based Channel Selection of Motor Imagery Electroencephalogram

MENG Ming; DONG Zhichao; GAO Yunyuan; KONG Wanzeng

doi:10.11999/JEIT210778

Volume 44 Issue 2

Feb. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(2): 477-485

MENG Ming, DONG Zhichao, GAO Yunyuan, KONG Wanzeng. Correlation and Sparse Representation Based Channel Selection of Motor Imagery Electroencephalogram[J]. Journal of Electronics & Information Technology, 2022, 44(2): 477-485. doi: 10.11999/JEIT210778

Citation:

MENG Ming, DONG Zhichao, GAO Yunyuan, KONG Wanzeng. Correlation and Sparse Representation Based Channel Selection of Motor Imagery Electroencephalogram[J]. Journal of Electronics & Information Technology, 2022, 44(2): 477-485. doi: 10.11999/JEIT210778

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Correlation and Sparse Representation Based Channel Selection of Motor Imagery Electroencephalogram

doi: 10.11999/JEIT210778

1.
School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
2.
Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China

Funds: The National Natural Science Foundation of China (61871427, 61971168, U20B2074)

Received Date: 2021-08-04
Accepted Date: 2021-12-13
Rev Recd Date: 2021-12-09

Available Online: 2021-12-25

Publish Date: 2022-02-25

Abstract

Abstract

In Motor Imagery (MI) based Brain Computer Interface (BCI), more channels of ElectroEncephaloGram (EEG) signal are usually adopted to improve the classification accuracy. But there will be channels containing irrelevant or redundant information about MI tasks, which degenerate the performance improvement of BCI. A Channel Selection method based on Correlation and Sparse Representation (CSR-CS) is proposed for EEG classification. Firstly, the Pearson correlation coefficient of each channel of the training sample is calculated to select the significant channels. Then the filter bank common spatial pattern features of the region where the significant channels are located are extracted and spliced into a dictionary. The number of non-zero sparse coefficients obtained from the dictionary is used to characterize the classification ability of each region, and the significant channels contained in the significant regions are selected as the optimal channels. Finally, the common spatial pattern and support vector machine are employed for feature extraction and classification respectively. In the classification experiments of two categories of MI task with BCI competition III dataset IVa and BCI competition IV dataset I, the average classification accuracy reaches 88.61% and 83.9%, which indicates the effectiveness and robustness of the proposed channel selection method.
- Brain Computer Interface (BCI),
- Motor Imagery (MI),
- Common spatial pattern,
- Support Vector Machine (SVM),
- Channel selection

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References(22)

References

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