Classification of Gamers Using Multiple Physiological Signals: Distinguishing Features of Internet Gaming Disorder

Abstract

The proliferating and excessive use of internet games has caused various comorbid diseases, such as game addiction, which is now a major social problem. Recently, the American Psychiatry Association classified “Internet gaming disorder (IGD)” as an addiction/mental disorder. Although many studies have been conducted on the diagnosis, treatment, and prevention of IGD, screening studies for IGD are still scarce. In this study, we classified gamers using multiple physiological signals to contribute to the treatment and prevention of IGD. Participating gamers were divided into three groups based on Young’s Internet Addiction Test score and average game time as follows: Group A, those who rarely play games; Group B, those who enjoy and play games regularly; and Group C, those classified as having IGD. In our game-related cue-based experiment, we obtained self-reported craving scores and multiple physiological data such as electrooculogram (EOG), photoplethysmogram (PPG), and electroencephalogram (EEG) from the users while they watched neutral (natural scenery) or stimulating (gameplay) videos. By analysis of covariance (ANCOVA), 13 physiological features (vertical saccadic movement from EOG, standard deviation of N-N intervals, and PNN50 from PPG, and many EEG spectral power indicators) were determined to be significant to classify the three groups. The classification was performed using a 2-layers feedforward neural network. The fusion of three physiological signals showed the best result compared to other cases (combination of EOG and PPG or EEG only). The accuracy was 0.90 and F-1 scores were 0.93 (Group A), 0.89 (Group B), and 0.88 (Group C). However, the subjective self-reported scores did not show a significant difference among the three groups by ANCOVA analysis. The results indicate that the fusion of physiological signals can be an effective method to objectively classify gamers.

Effects of Game-Related Tasks for the Diagnosis and Classification of Gaming Disorder

“…Therefore, increased theta band power in the LF during online video gaming versus CPT in the HC group indicates that online video game play requires more cognitive control than CPT. In contrast, the difference in the amount of LF theta power increase between HC and GD during online video game play-i.e., a lower theta power increase in the GD group compared with the HC group-may indicate a loss of cognitive control in the GD participants due to a deterioration of prefrontal function [45,46].…”

Multiple-Kernel Support Vector Machine for Predicting Internet Gaming Disorder Using Multimodal Fusion of PET, EEG, and Clinical Features

“…In the previous study, Dyrba et al ( 2015 ) reported that the integrating multimodal MRI data showed improved classification accuracy compared to utilizing the best single measures by multiple-kernel SVM. In the study with IGD, multiple physiological markers, such as electrooculogram (EOG), photoplethysmogram (PPG), and electroencephalogram (EEG), were utilized to classify individuals who seldom play games, those who enjoy and play games frequently, and those who have IGD (Ha et al, 2021 ). According to a two-layer feedforward neural network model, the combination of three physiological signals had a higher classification accuracy (90%) than the combination of EOG and PPG or EEG only.…”