Analysis on EEG signal with machine learning

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

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

5 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2019 International Conference on Image and Video Processing, and Artificial Intelligence
EditorsRuidan Su
PublisherSPIE
ISBN (Electronic)9781510634091
DOIs
Publication statusPublished - 2019
Event2019 2nd International Conference on Image and Video Processing, and Artificial Intelligence, IVPAI 2019 - Shanghai, China
Duration: 23 Aug 201925 Aug 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11321
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference2019 2nd International Conference on Image and Video Processing, and Artificial Intelligence, IVPAI 2019
Country/TerritoryChina
CityShanghai
Period23/08/1925/08/19

Keywords

  • Brain computer interface (BCI)
  • Decision making
  • Electroencephalogram (EEG)
  • Neural network
  • Principal component analysis (PCA)

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