Deep Learning on Game Addiction Detection Based on Electroencephalogram

Abstract

Playing games for too long can be addictive. Based on a recent study by Brand et al, adolescents are considered more vulnerable than adults to game addiction. The activity of playing games produces a wave in the brain, namely beta waves where the person is in a focused state. Brain wave activity can be measured and captured using an Electroencephalogram (EEG). Recording brain wave activity naturally requires a prominent and constant brain activity such as when concentrating while playing a game. This study aims to detect game addiction in late adolescence by applying Convolutional Neural Network (CNN). Recording of brain waves was carried out three times for each respondent with a stimulus to play three different games, namely games included in the easy, medium, and hard categories with a consecutive taking time of 10 minutes, 15 minutes, and 30 minutes. Data acquisition results are feature extraction using Fast Fourier Transform to get the average signal for each respondent. Based on the research conducted, obtained an accuracy of 86% with a loss of 0.2771 where the smaller the loss value, the better the CNN model built. The test results on the model produce an overall accuracy of 88% with misclassification in 1 data. The CNN model built is good enough for the detection of game addiction in late adolescence.

Severity identification for internet gaming disorder using heart rate variability reactivity for gaming cues: a deep learning approach

“…Deep learning models have been mainly used to classify psychiatric disorders through neuroimaging findings (35), but some studies have also been conducted on HRV data (36). There were previous studies that applied deep learning models to IGD (37,38), but no studies using HRV data. Using a deep learning model, the usefulness of HRV in assessing IGD can be explored, and which HRV parameters are extracted for classifying IGD can be explored.…”