Analysis on EEG signal with machine learning

Jaehoon Cha; Kyeong Soo Kim; Haolan Zhang; Sanghyuk Lee

doi:10.1117/12.2548313

27 November 2019 Analysis on EEG signal with machine learning

Jaehoon Cha, Kyeong Soo Kim, Haolan Zhang, Sanghyuk Lee

Proceedings Volume 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence; 113212E (2019) https://doi.org/10.1117/12.2548313
Event: The Second International Conference on Image, Video Processing and Artifical Intelligence, 2019, Shanghai, China

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.

Citation Download Citation

Jaehoon Cha, Kyeong Soo Kim, Haolan Zhang, and Sanghyuk Lee "Analysis on EEG signal with machine learning", Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113212E (27 November 2019); https://doi.org/10.1117/12.2548313

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