An EEG Feature Extraction Algorithm Based on Adaptive Time-frequency Motor Imagery

To solve the deficiency of traditional common spatial pattern feature extraction algorithm due to the changes of neurophysiology in subjects with the variation of time and frequency domain, a self-adaptive time-frequency domain common spatial pattern feature extraction algorithm is presented. The al...

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Bibliographic Details
Published in2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA) pp. 1016 - 1021
Main Authors Wu, Yan, Mao, ZhiDa, Tang, LiMin, Li, Qi
Format Conference Proceeding
LanguageEnglish
Published IEEE 20.05.2022
Subjects
adaptive
brain-computer interface
Brain-computer interfaces
Classification algorithms
common spatial pattern
Data models
Electroencephalography
Feature extraction
motor imagery (MI)
Robustness
Time-frequency analysis
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Summary:To solve the deficiency of traditional common spatial pattern feature extraction algorithm due to the changes of neurophysiology in subjects with the variation of time and frequency domain, a self-adaptive time-frequency domain common spatial pattern feature extraction algorithm is presented. The algorithm finds out the personalized time and frequency domain of the subject automatically by a data-driven method. The results demonstrated that the algorithm improves the performance of the traditional common spatial pattern. In addition, the regularization term of the algorithm is designed. The experiment results on two public competition data sets show that the classification accuracies of the proposed adaptive feature extraction method is higher than those of the original public model (10.9% and 20.33%).
DOI:10.1109/CVIDLICCEA56201.2022.9824688