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감성판별을 위한 생체신호기반 특징선택 분류기 설계

Other Titles
 The Design of Feature Selection Classifier based on Physiological Signal for Emotion Detection 
 이지은  ;  유선국 
 Journal of the Institute of Electronics and Information Engineers of Korea (전자공학회논문지), Vol.50(11) : 206-216, 2013 
Journal Title
Journal of the Institute of Electronics and Information Engineers of Korea(전자공학회논문지)
Issue Date
Emotion ; Physiological signal ; Support vector machine ; Genetic algorithm
The emotion plays a critical role in human’s daily life including learning, action, decision and communication. In this paper, emotion discrimination classifier is designed to reduce system complexity through reduced selection of dominant features from biosignals. The photoplethysmography(PPG), skin temperature, skin conductance, fontal and parietal electroencephalography(EEG) signals were measured during 4 types of movie watching associated with the induction of neutral, sad, fear joy emotions. The genetic algorithm with support vector machine(SVM) based fitness function was designed to determine dominant features among 24 parameters extracted from measured biosignals. It shows maximum classification accuracy of 96.4%, which is 17% higher than that of SVM alone. The minimum error features selected are the mean and NN50 of heart rate variability from PPG signal, the mean of PPG induced pulse transit time, the mean of skin resistance, and δ and β frequency band powers of parietal EEG. The combination of parietal EEG, PPG, and skin resistance is recommendable in high accuracy instrumentation, while the combinational use of PPG and skin conductance(79% accuracy) is affordable in simplified instrumentation.
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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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