EEG signal motion recognition based on GSA-SVM

Zhicheng Liu; Ying Chang; Hongyinxun Zhou; Hao Zhang; Yu Feng; Weibin Zhang; Ze Zhang; Ming Liu

doi:10.1117/12.3029160

22 May 2024 EEG signal motion recognition based on GSA-SVM

Zhicheng Liu, Ying Chang, Hongyinxun Zhou, Hao Zhang, Yu Feng, Weibin Zhang, Ze Zhang, Ming Liu

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 131761H (2024) https://doi.org/10.1117/12.3029160
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

This paper reviews the research progress of gait recognition and rehabilitation training control based on EEG signals. Firstly, the importance and application scenarios of gait recognition research were introduced, with a focus on discussing gait recognition methods based on EEG signals. Then, the basic characteristics of EEG signals and the main EEG signal analysis methods used for gait recognition were summarized, including time-domain analysis, frequency-domain analysis, and time-frequency analysis. Secondly, this article collected some motion EEG signals and selected corresponding recognition algorithms through data processing, feature extraction, and classifier selection to perform gait recognition on the EEG signals. The results show that the algorithm proposed in this paper has a high recognition rate.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhicheng Liu, Ying Chang, Hongyinxun Zhou, Hao Zhang, Yu Feng, Weibin Zhang, Ze Zhang, and Ming Liu "EEG signal motion recognition based on GSA-SVM", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 131761H (22 May 2024); https://doi.org/10.1117/12.3029160

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