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http://dx.doi.org/10.9717/kmms.2015.18.5.599

Comparison of EEG during Watching Emotional Videos according to the Degree of Smartphone Addiction  

Kim, Seul-Kee (Dept. of media Engineering, Catholic University of Korea)
Kim, So-Yeong (Dept. of media Engineering, Catholic University of Korea)
Kang, Hang-Bong (Dept. of media Engineering, Catholic University of Korea)
Publication Information
Abstract
As smartphone usage has increased recently, so has smartphone addiction. Many of the smartphone users, however, do not even recognize the risk of smartphone addiction. In this experiment, smartphone users have been categorized into two groups by smartphone addiction measure (S-measure) developed by 2011 National Information Society Agency (NIA): A high risk group and a normal group. The changes of brain waves have been observed when the subjects were watching emotional videos of anger, sadness, happiness, and fear. The results show that the values of FP1 and FP2 (frontal lobe) theta band of the high risk group have been measured to be high, which indicate anxiety disorder. Although happiness and fear videos showed no difference between these groups, sadness and anger videos showed significantly different results for these groups: the brain waves of the high risk group showed higher values than those of the normal group. Therefore, this experiment showed that the high risk group takes feelings of sadness and anger more sensitively than the normal group.
Keywords
Electroencephalogram (EEG); Power Spectrum; Smartphone Addiction; Watching Emotional Videos;
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1 Y. Lin, C. Wang, T. Jung, T. Wu, S. Jeng, J. Duann et al. “EEG-Based Emotion Recognition in Music Listening,” IEEE Transactions on Biomedical Engineering, Vol. 57, No. 7, pp. 1798-1806, 2010.   DOI   ScienceOn
2 D.O. Bos, “EEG-based Emotion Recognition ,” The Influence of Visual and Auditory Stimuli, University of Twente, pp.1-17, 2006.
3 A.T. Sohaib, S. Qureshi, J. Hagelbäck, O. Hilborn, and P. Jerčić. "Evaluating Classifiers for Emotion Recognition using EEG," Proceeding of Foundations of Augmented Cognition, pp. 492-501, 2013.
4 NIA, Development of Korean Smartphone Addiction Proneness Scale for Youth and Adults, NIA IV-RER-11051, 2011.
5 H. Kang and C. Park, “Development and Validation of the Smartphone Addiction Inventory,” Korea Journal of Psychology: General, Vol. 31, No. 2, pp. 563-580, 2012.
6 D. Nie, X. Wang, L. Shi, and B. Lu, “EEG-based Emotion Recognition during Watching Movies,” Proceeding of 5th International IEEE/EMBS Conference, pp. 667-670, 2011.
7 H. Lee, H. Ahn, S. Choi, and W. Choi, “The SAMS: Smartphone Addiction Management System and Verification,” Journal of Medical Systems, Vol. 38, No. 1, pp. 1-10, 2014.   DOI
8 C. Jenaro, N. Flores, M.M. Vela, F.G.L. Gil, and C. Caballo, “Problematic Internet and Cell-phone Use: Psychological, Behavioral, and Health Correlates,” Addiction Research and Theory, Vol. 15, No. 3, pp. 309-320, 2007.   DOI
9 H. Gim Y. Jang, E. Jeong, and S. Ryu, “A Study on the Relationship among College Students’ Construal Level, Self-control and Smartphone Addictive Use,” Journal of Future Oriented Youth Society Vol. 10, No. 2, pp. 47-67, 2012.
10 B. Kim, E. Ko, and H. Choi, “A Study on Actors Affecting Smart-phone Addiction in University Students : a Focus on Differences in Classifying Risk Groups,” Korean Journal of Youth Studies, Vol. 24, No. 3, pp. 67-98, 2013.
11 M. Soleymani, M. Pantic, and T. Pun, "Multimodal Emotion Recognition in Response to Videos," IEEE Transactions on Affective Computing, Vol. 3, No. 2, pp. 211-223, 2012.   DOI
12 A.C. Conneau and S. Essid. "Assessment of New Spectral Features for EEG-based Emotion Recognition," Proceeding of 2014 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 4698-4702, 2014.
13 Russell and A. James, “A Circumplex Model of Affect,” Journal of Personality and Social Psychology, Vol. 39, No. 6, pp. 1161-1178, 1980.   DOI
14 K. Hwang, Y. Yoo, and O. Cho, “Smartphone Overuse and Upper Extremity Pain, Anxiety, Depression, and Interpersonal Relationships among College Students,” Journal of the Korea Contents Association, Vol. 12, No. 10, pp. 365-375, 2012.   DOI   ScienceOn
15 J. Lee and H. Kang, “EEG and ERP based Degree of Internet Game Addiction Analysis,” Journal of the Korea Multimedia Society, Vol. 17, No.11, pp. 1325-1334, 2014.   DOI
16 Y. Liu, O. Sourina, and M.K. Nguyen, “Real-time EEG-based Emotion Recognition and its Applications,” Transactions on Computational Science XII, Vol. 12, pp. 256-277, 2011.