Development of a Diagnostic Algorithm to Identify Psycho-Physiological Game Addiction Attributes Using Statistical Parameters | Zendy

Maria Hafeez | Zendy; Muhammad Dawood Idrees | Zendy; Jung-Yong Kim | Zendy

Open Access

Development of a Diagnostic Algorithm to Identify Psycho-Physiological Game Addiction Attributes Using Statistical Parameters

Author(s) -

Maria Hafeez,

Muhammad Dawood Idrees,

Jung-Yong Kim

Publication year - 2017

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2017.2753287

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Over the past decade, there has been a significant increase in research examining the various aspects of mobile game addiction diagnosis and treatment using different scales and questionnaires. The aim of this paper was to examine the frequency attributes of the EEGs (electroencephalographs) of addicted and non-addicted mobile game players to detect the early signs of game addiction using physiological parameters and to design a framework for the use of these results to alert for potential game addiction. This research comprises two parts. The first part addresses the diagnosis of mobile game addiction psycho-physiologically, and the second part consists of a design to implement the results of the proposed diagnostic tests practically to detect mobile game addiction using a wearable mobile addiction sensing system. The comprehensive scale for assessing game behavior manual from 2010 was used to record the basic demographic information and pre-categorization regarding the game addiction. Temporal and frequency domain analysis were applied to the electroencephalographic data from all the subjects to acquire quantitative information to identify mobile game players with addiction. Finally, logistic regression modeling was employed to quantify the parameters that can be used as decision variables to identify the subject's category. The overall trend in alpha and theta frequencies was observed to be dominant and distinctive compared with the other frequencies in the occipital region of subjects with addiction. This paper reveals that the parameterization of EEG signals from the occipital region can provide evidential proof to identify mobile game addicts.

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