Abstract
In order to improve the EEG recognition accuracy and real-time performance, a classification and recognition method for optimizing the penalty factor C and kernel parameter g of Support Vector Machine (SVM) based on Particle Swarm Optimization (PSO) algorithm is proposed in this paper. Firstly, the Regularization Common Spatial Pattern (R-CSP) was used for EEG feature extraction. Secondly, the penalty factor and the kernel function were optimized by the proposed PSO algorithm. Finally, the constructed SVM classifiers were trained and tested by the two class EEG data of right foot and right hand movements. The experimental results show that the recognition rate for EEG classification of the PSO-SVM is average 2.2% higher than the non-parameter-optimized SVM classifier, and it is significantly higher than the traditional LDA classifier, which proves the feasibility and higher accuracy of the algorithm.
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This research was funded by the Natural Science Foundation of Zhejiang Province grant number LY18F030009 and the National Natural Science Foundation of China grant number 61971168, 61372023.
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Li, X., Ma, Y., Zhang, Q., Gao, Y. (2021). EEG Characteristics Extraction and Classification Based on R-CSP and PSO-SVM. In: Liu, Q., Liu, X., Shen, T., Qiu, X. (eds) The 10th International Conference on Computer Engineering and Networks. CENet 2020. Advances in Intelligent Systems and Computing, vol 1274. Springer, Singapore. https://doi.org/10.1007/978-981-15-8462-6_189
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DOI: https://doi.org/10.1007/978-981-15-8462-6_189
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