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http://dx.doi.org/10.14400/JDC.2019.17.3.221

Beta-wave Correlation Analysis Model based on Unsupervised Machine Learning  

Choi, Sung-Ja (Department of software, College of IT, Gachon University)
Publication Information
Journal of Digital Convergence / v.17, no.3, 2019 , pp. 221-226 More about this Journal
Abstract
The characteristic of the beta wave among the EEG waves corresponds to the stress area of human perception. The over-bandwidth of the stress is extracted by analyzing the beta-wave correlation between the low-bandwidth and high-bandwidth. We present a KMeans clustering analysis model for unsupervised machine learning to construct an analytical model for analyzing and extracting the beta-wave correlation. The proposed model classifies the beta wave region into clusters of similar regions and identifies anomalous waveforms in the corresponding clustering category. The abnormal group of waveform clusters and the normal category leaving region are discriminated from the stress risk group. Using this model, it is possible to discriminate the degree of stress of the cognitive state through the EEG waveform, and it is possible to manage and apply the cognitive state of the individual.
Keywords
Machine Learning; Unsupervised Learning; KMeans; SPARK; EEG;
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Times Cited By KSCI : 3  (Citation Analysis)
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