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http://dx.doi.org/10.15207/JKCS.2020.11.2.017

EEG Signal Classification based on SVM Algorithm  

Rhee, Sang-Won (Department of Science Education, Daegu University)
Cho, Han-Jin (Department of Smart & PhotoVoltaic Convergence, Far East University)
Chae, Cheol-Joo (Department of General Education, Korea National College of Agriculture and Fisheries)
Publication Information
Journal of the Korea Convergence Society / v.11, no.2, 2020 , pp. 17-22 More about this Journal
Abstract
In this paper, we measured the user's EEG signal and classified the EEG signal using the Support Vector Machine algorithm and measured the accuracy of the signal. An experiment was conducted to measure the user's EEG signals by separating men and women, and a single channel EEG device was used for EEG signal measurements. The results of measuring users' EEG signals using EEG devices were analyzed using R. In addition, data in the study was predicted using a 80:20 ratio between training data and test data by applying a combination of specific vectors with the highest classifying performance of the SVM, and thus the predicted accuracy of 93.2% of the recognition rate. This paper suggested that the user's EEG signal could be recognized at about 93.2 percent, and that it can be performed only by simple linear classification of the SVM algorithm, which can be used variously for biometrics using EEG signals.
Keywords
EEG; SVM; Unsupervised Learning; Data Classification; Authentication;
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Times Cited By KSCI : 4  (Citation Analysis)
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