Psychophysiological Identification of Game Addicts and Non-Addicts by Statistical Modelling with EEG Data (2018)

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Other Titles

게임플레이어의 몰입과 중독 상태에 태한 심리 생리학적 분석및 분류


Maria Hafeez

Alternative Author(s)



Jung Yong Kim

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During recent years gaming addiction has received increased attention from psychologist, psychiatrist, parents, teachers, media, and mental health organizations and, to some extent, by gamers all over the world. Some researchers use the terminology of problematic or excessive game usage instead of disorder to denote the harmful use of video game playing. Based on published empirical studies, most of them from early 2000 to date, it appears that excessive game play or game addiction have potentially damaging effects on individuals, in the same way as other traditional addictions, including substance use addictions. Moreover, there is no uniform, psychological or physiological screening criteria available and scope has been hindered by use of inconsistent and non-standardized criteria to detect mobile game addiction. Most of the recruitment methods have serious sampling biases with over reliance on self-selected samples. Clearly there exist a gap in current established understanding of gaming addiction. There is a need of epidemiological research to determine the occurrence and prevalence of clinically significant problems that are associated with gaming addiction, to ensure recovery and treatment in a better way. A design is suggested to cope with these problems which diagnoses the game addiction physiologically from encephalographic data and implements the achieved results in form of a device or application to use them practically as a warning to be caught in game addiction. This study examines the frequency and time domain attributes of EEG to seek the possibility of detecting any distinction between addicted and non-addicted mobile game players. Comprehensive Scale for Assessing Game Behavior (CGS) Manual 2010 was used to record the basic demographic information and pre-categorization regarding game device. The EEG signal was analyzed in time and frequency domains to carry out a detailed research on the correlates of mobile game addiction differentiating the two categories, i.e. addicted and non-addicted players. The analysis in both the time and frequency domains simultaneously helped to discriminate region of scalp and specific frequency among the two groups. Cross correlation and observations from the study of total power of spectral data helped to concentrate on occipital region. Further detailed analysis helped to find the specific frequency distinguishing the addicted subjects from non-addicted subjects. T-test was performed to verify the descriptive differences in mean and standard deviation of power spectrum values between addicted and non-addicted subject groups. It was observed that the variation in mean values and spread of standard deviation of spectral data of addicted subjects was highly greater than non-addicted subjects. The overall trend in alpha, beta and theta frequencies was observed to be dominant and distinctive than other frequencies in addicted subjects. A Logistic regression model was fit to the spectral data from the occipital region. The model was tested after training with the available samples and the prediction accuracy confirmed that the model can be applied as a practical tool to diagnose game addiction by using the EEG signal from occipital region by using the modelling of theta frequency components only. A design is suggested based on the statistical tools and regression modelling, which can be used as application in mobile devices. EEG data may be collected from commercially available, single or double electrode/sensor headsets. The data can be embedded in the application, which will compute the addiction detection level. Like health application in mobile devices, we can use game addiction application to keep track of our activities and may be warned if crossing the limits of healthy playing.


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