Emotional Stress State Detection Using Genetic Algorithm-Based Feature Selection on EEG Signals

Dongkoo Shon, Kichang Im, Jeong-Ho Park, Dong-Sun Lim, Byungtae Jang and Jong-Myon Kim
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Dongkoo Shon: School of Computer Engineering and Information Technology, University of Ulsan, Ulsan 44610, Korea
Kichang Im: ICT Convergence Safety Research Center, University of Ulsan, Ulsan 44610, Korea
Jeong-Ho Park: Industry IT Convergence Research Group, Intelligent Robotics Research Division, SW Contents Research Laboratory, Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Korea
Dong-Sun Lim: Industry IT Convergence Research Group, Intelligent Robotics Research Division, SW Contents Research Laboratory, Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Korea
Byungtae Jang: Industry IT Convergence Research Group, Intelligent Robotics Research Division, SW Contents Research Laboratory, Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Korea
Jong-Myon Kim: School of Computer Engineering and Information Technology, University of Ulsan, Ulsan 44610, Korea

IJERPH, 2018, vol. 15, issue 11, 1-11

Abstract: In recent years, stress analysis by using electro-encephalography (EEG) signals incorporating machine learning techniques has emerged as an important area of research. EEG signals are one of the most important means of indirectly measuring the state of the brain. The existing stress algorithms lack efficient feature selection techniques to improve the performance of a subsequent classifier. In this paper, genetic algorithm (GA)-based feature selection and k -nearest neighbor ( k -NN) classifier are used to identify stress in human beings by analyzing electro-encephalography (EEG) signals. GA is incorporated in the stress analysis pipeline to effectively select subset of features that are suitable to enhance the performance of the k -NN classifier. The performance of the proposed method is evaluated using the Database for Emotion Analysis using Physiological Signals (DEAP), which is a public EEG dataset. A feature set is extracted in 32 EEG channels, which consists of statistical features, Hjorth parameters, band power, and frontal alpha asymmetry. The selected features through GA are used as input to the k -NN classifier to distinguish whether each EEG datapoint represents a stress state. To further consolidate, the effectiveness of the proposed method is compared with that of a state-of-the-art principle component analysis (PCA) method. Experimental results show that the proposed GA-based method outperforms PCA, with GA demonstrating 71.76% classification accuracy compared with 65.3% for PCA. Thus, it can be concluded that the proposed method can be effectively used for stress analysis with high classification accuracy.

Keywords: stress detection; k -nearest neighbors; genetic algorithm; machine learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2018
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