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비선형 반복 패턴과 스펙트럼 분석을 이용한 집중-비집중 분류기의 성능 평가

Other Titles
 Performance Evaluation of Attention-inattetion Classifiers using Non-linear Recurrence Pattern and Spectrum Analysis 
 이지은  ;  유선국  ;  이병채 
 Korean Journal of the Science of Emotion & Sensibility (감성과학), Vol.16(3) : 149-156, 2013 
Journal Title
Korean Journal of the Science of Emotion & Sensibility(감성과학)
Issue Date
attention ; classifier ; non-linear analysis ; spectrum analysis
Attention is one of important cognitive functions in human affecting on the selectional concentration of relevant events and ignorance of irrelevant events. The discrimination of attentional and inattentional status is the first step to manage human's attentional capability using computer assisted device. In this paper, we newly combine the non-linear recurrence pattern analysis and spectrum analysis to effectively extract features(total number of 13) from the electroencephalographic signal used in the input to classifiers. The performance of diverse types of attention-inattention classifiers, including supporting vector machine, back-propagation algorithm, linear discrimination, gradient decent, and logistic regression classifiers were evaluated. Among them, the support vector machine classifier shows the best performance with the classification accuracy of 81 %. The use of spectral band feature set alone(accuracy of 76 %) shows better performance than that of non-linear recurrence pattern feature set alone(accuracy of 67 %). The support vector machine classifier with hybrid combination of non-linear and spectral analysis can be used in later designing attention-related devices.
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Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers
Yonsei Authors
Yoo, Sun Kook(유선국) ORCID logo https://orcid.org/0000-0002-6032-4686
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