@inproceedings{7eb58cb867a840b8be106a1d83cb2793,
title = "Analysis on EEG signal with machine learning",
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.",
keywords = "Brain computer interface (BCI), Decision making, Electroencephalogram (EEG), Neural network, Principal component analysis (PCA)",
author = "Jaehoon Cha and Kim, {Kyeong Soo} and Haolan Zhang and Sanghyuk Lee",
note = "Publisher Copyright: {\textcopyright} 2019 SPIE.; 2019 2nd International Conference on Image and Video Processing, and Artificial Intelligence, IVPAI 2019 ; Conference date: 23-08-2019 Through 25-08-2019",
year = "2019",
doi = "10.1117/12.2548313",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Ruidan Su",
booktitle = "2019 International Conference on Image and Video Processing, and Artificial Intelligence",
}