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. 2023 May 4;23(1):816.
doi: 10.1186/s12889-023-15750-4.

Measurement of craving among gamers with internet gaming disorder using repeated presentations of game videos: a resting-state electroencephalography study

Sangin Park  1 , Jihyeon Ha  2 , Wonbin Ahn  3 , Laehyun Kim  4   5
Affiliations

Measurement of craving among gamers with internet gaming disorder using repeated presentations of game videos: a resting-state electroencephalography study

Sangin Park et al. BMC Public Health. .

Abstract

Background: Internet gaming disorder (IGD) is receiving increasing attention owing to its effects on daily living and psychological function.

Methods: In this study, electroencephalography was used to compare neural activity triggered by repeated presentation of a stimulus in healthy controls (HCs) and those with IGD. A total of 42 adult men were categorized into two groups (IGD, n = 21) based on Y-IAT-K scores. Participants were required to watch repeated presentations of video games while wearing a head-mounted display, and the delta (D), theta (T), alpha (A), beta (B), and gamma (G) activities in the prefrontal (PF), central (C), and parieto-occipital (PO) regions were analyzed.

Results: The IGD group exhibited higher absolute powers of DC, DPO, TC, TPO, BC, and BPO than HCs. Among the IGD classification models, a neural network achieves the highest average accuracy of 93% (5-fold cross validation) and 84% (test).

Conclusions: These findings may significantly contribute to a more comprehensive understanding of the neurological features associated with IGD and provide potential neurological markers that can be used to distinguish between individuals with IGD and HCs.

Keywords: Behavior addiction; Craving; Electroencephalography; Internet gaming disorder; Repetitive stimulations; Resting-state.

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Conflict of interest statement

The author(s) declare(s) that they have no competing interests.

Figures

Fig. 1
Fig. 1
a Procedure for stimuli selection. b Examples of still images from the game (top; FIFA Online 3, Sudden Attack, and League of Legends) and neutral (bottom) videos
Fig. 2
Fig. 2
Experimental procedure (left) and environment (right)
Fig. 3
Fig. 3
Results of subjective ratings for craving experience (mean and scores from 36 trials) in the healthy controls (HCs) and internet gaming disorder (IGD) groups (*** p < 0.001)
Fig. 4
Fig. 4
Comparisons of averaged delta power in prefrontal, central, and parieto-occipital regions between healthy control (HC) and internet gaming disorder (IGD) groups in both pre- and post-resting states. a Results of two-way ANOVA. b Electroencephalogram (EEG) topography in the delta band (1–4 Hz) in pre- and post-resting states
Fig. 5
Fig. 5
Comparisons of averaged theta power in prefrontal, central, and parieto-occipital regions between healthy control (HCs) and internet gaming disorder (IGD) groups in both pre- and post-resting states. a Results of two-way ANOVA. b Electroencephalogram (EEG) topography in the theta band (4–8 Hz) in pre- and post-resting states
Fig. 6
Fig. 6
Comparisons of averaged alpha power in prefrontal, central, and parieto-occipital regions between healthy control (HC) and internet gaming disorder (IGD) groups in both pre- and post-resting states. a Results of two-way ANOVA. b Electroencephalogram (EEG) topography in the alpha band (8–13 Hz) in pre- and post-resting states
Fig. 7
Fig. 7
Comparisons of averaged beta power in prefrontal, central, and parieto-occipital regions between healthy control (HC) and internet gaming disorder (IGD) groups in both pre- and post-resting states. a Results of two-way ANOVA. b Electroencephalogram (EEG) topography in the beta band (13–30 Hz) for in pre- and post-resting states
Fig. 8
Fig. 8
Comparisons of averaged gamma power in prefrontal, central, and parieto-occipital regions between healthy control (HC) and internet gaming disorder (IGD) groups in both pre- and post-resting states. a Results of two-way ANOVA. b Electroencephalogram (EEG) topography in the gamma band (30–50 Hz) in pre- and post-resting states
Fig. 9
Fig. 9
Receiver operating characteristics curves for three classifiers on (a) 5-fold cross-validation and (b) Test
Fig. 10
Fig. 10
The distribution for the three classifiers for the permutation test (p < 0.01). a Discriminant Analysis. b Support Vector Machine. c Neural Network
See this image and copyright information in PMC

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