A Motion Imagery EEG Signal Recognition Algorithm Based on Power Spectral Density combined with Particle Swarm Optimization Algorithm Optimized Support Vector Machine-Reference-Cited by-同舟云学术

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A Motion Imagery EEG Signal Recognition Algorithm Based on Power Spectral Density combined with Particle Swarm Optimization Algorithm Optimized Support Vector Machine

Published:2022-10 Issue: Volume: Page:
ISSN:
Container-title:2022 2nd International Conference on Networking Systems of AI (INSAI)
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Author:

Lin Ruijing¹,Dong Chaoyi¹,Ma Pengfei¹,Chen Xiaoyan¹,Liu Huanzi¹,Lei Dongyang¹

Affiliation:

1. College of Electric Power Inner Mongolia University of technology,Inner Mongolia Key Laboratory of Mechanical and Electrical Control,Hohhot,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10175092/10175079/10175148.pdf?arnumber=10175148

Reference16 articles.

1. Classifying Motor-imagination Signals in Brain-computer Interface Based on Feature Extraction of Parametric AR Model

2. An effective feature extraction method by power spectral density of EEG signal for 2-class motor imagery-based BCI

3. A feature extraction and classification algorithm based on PSO-CS P-SVM for motor imagery EEG signals;liu;Journal of Central South University (Science and Technology),2020

4. Hilbert transform and RBF-kernel based support vector machine synergy for automatic classification of EEG signals;vipani;Int J Latest Trends Eng Technol,2018

5. Feature extraction method based on enhanced power spectral density for emotion analysis using EEG;luo;Chinese journal of medical physics,2022

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