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Proceedings Paper

Analysis on EEG signal with machine learning
Author(s): Jaehoon Cha; Kyeong Soo Kim; Haolan Zhang; Sanghyuk Lee
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Paper Abstract

In this paper, research on electroencephalogram (EEG) is carried out through principal component analysis (PCA) and support vector machine (SVM). PCA is used to collect EEG data characteristics to discriminate the behaviors by SVM methodology. The actual EEG signals are obtained from 18 experimenters who raised hands with meditation and actual movement during the experiments. The 16-channel data from the experiments form one data set. In order to get principal component of EEG signal, 16 features are considered from each channel and normalized. Simulation results demonstrate that two behaviors – i.e., raising hands and meditation – can be clearly classified using SVM, which is also visualized by a 2-dimensional principal component plot. Our research shows that specific human actions and thinking can be efficiently classified based on EEG signals using machine learning techniques like PCA and SVM. The result can apply to make action only with thinking.

Paper Details

Date Published: 27 November 2019
PDF: 6 pages
Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113212E (27 November 2019); doi: 10.1117/12.2548313
Show Author Affiliations
Jaehoon Cha, Xi'an Jiaotong-Liverpool Univ. (China)
Kyeong Soo Kim, Xi'an Jiaotong-Liverpool Univ. (China)
Haolan Zhang, Zhejiang Univ. (China)
Sanghyuk Lee, Xi'an Jiaotong-Liverpool Univ. (China)


Published in SPIE Proceedings Vol. 11321:
2019 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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