Research Article
Feature Extraction Method of EEG Signal Based on Synchroextracting Transform
@INPROCEEDINGS{10.1007/978-3-030-82565-2_38, author={Lin Han and Liang Lu and Haoran Dong and Shuangbo Xie and Gang Yu and Tao Shen and Mingxu Sun and Tianyi Wang and Xuqun Pei}, title={Feature Extraction Method of EEG Signal Based on Synchroextracting Transform}, proceedings={Multimedia Technology and Enhanced Learning. Third EAI International Conference, ICMTEL 2021, Virtual Event, April 8--9, 2021, Proceedings, Part II}, proceedings_a={ICMTEL PART 2}, year={2021}, month={7}, keywords={Synchroextracting Transform Genetic algorithm Support vector machine Brain-Computer Interface}, doi={10.1007/978-3-030-82565-2_38} }- Lin Han
Liang Lu
Haoran Dong
Shuangbo Xie
Gang Yu
Tao Shen
Mingxu Sun
Tianyi Wang
Xuqun Pei
Year: 2021
Feature Extraction Method of EEG Signal Based on Synchroextracting Transform
ICMTEL PART 2
Springer
DOI: 10.1007/978-3-030-82565-2_38
Abstract
Brain-Computer Interface (BCI) can convert the electrical activity signal of the cerebral cortex into a computer or other machine language to directly control external equipment. Aiming at the problem of low recognition accuracy of visual stimulation Electroencephalogram (EEG) signals. This paper adopts a method of EEG signal feature extraction based on Synchroextracting Transform (SET). The mean value filter method is used to remove the noise in EEG signal, and the time-frequency energy of EEG signal is taken as the characteristic parameter. Finally, the signal characteristics are input into the SVM model as characteristic parameters. The experimental results show that SET can extract the characteristic energy of EEG signal well and improve the resolution of signal.
