Computer Gaming and Physiological Changes in the Brain: An Insight from QEEG Complexity Analysis

Abstract

To compare the pattern of brain waves in video game addicts and normal individuals, a case-control study was carried out on both. Thirty participants were recruited from 14 to 20 years old males from two gaming centers. Twenty healthy participants were gathered from different schools in Tehran using the available sampling method. The QEEG data collection was performed in three states: closed-eye and open-eye states, and during a working memory task. As expected, the power ratios did not show a significant difference between the two groups. Regarding our interest in the complexity of signals, we used the Higuchi algorithm as the feature extractor to provide the input materials for the multilayer perceptron classifier. The results showed that the model had at least a 95% precision rate in classifying the addicts and healthy controls in all three types of tasks. Moreover, significant differences in the Higuchi Fractal Dimension of a few EEG channels have been observed. This study confirms the importance of brain wave complexity in QEEG data analysis and assesses the correlation between EEG-complexity and gaming disorder. Moreover, feature extraction by Higuchi algorithm can render support vector machine classification of the brain waves of addicts and healthy controls more accurate.

Correlation between electroencephalographic markers in the healthy brain

“…The dynamic marker HFD is the marker of the highest effectiveness and is expected to incorporate a maximal part of the information from the EEG signal. This conclusion is supported by many studies where HFD has been successfully used for the detection of small alterations in EEG related to different factors such as depression, anxiety, or microwave radiation 22,[37][38][39][40] . The traditional EEG band power marker ABP is the most commonly used band power marker which has shown good sensitivity in various applications 2,4,6,7 .…”