Effect of the involvement degree of playing video games on brain waves for an hour

Lin, Fang-Tsan; Chang, Chia Ling; Jou, Yung‐Tsan; Pan, S. C.; Hsu, Tsung-Yuan; Huang, Chuen-Der

doi:10.1109/icieem.2010.5646446

Cited by 7 publications

(4 citation statements)

References 9 publications

(4 reference statements)

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“…The P4 electrode was placed in the right Parietal and Angular gyri (BA39) and such alternation have been previously reported using fMRI in resting state. (4) A nearly significant difference in the closed-eye state in channel F3; previously, Lin et al had observed alpha band alternation in this channel in addicts (Lin et al, 2010).…”

Section: Discussionmentioning

confidence: 93%

“…This alternation of brain wave complexity in the right occipital region has not been previously reported in brain imaging studies. (2) A significant difference in the closed-eye state in channel F4; Lin et al had previously reported alpha band alternation in this channel in addicts (Lin et al, 2010). (3) A nearly significant difference in the closed-eye state in channel P4.…”

Section: Discussionmentioning

confidence: 95%

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

Hosseini

Delpazirian

Lanjanian

et al. 2021

Appl Psychophysiol Biofeedback

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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.

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Section: Discussionmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 95%

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

Hosseini

Delpazirian

Lanjanian

et al. 2021

Appl Psychophysiol Biofeedback

View full text Add to dashboard Cite

show abstract

“…The research subjects totaled 12 people and were divided into two groups based on differences in the level of involvement in playing video games. There was no significant change in the alpha and beta signals in the four lobes after being given a stimulus to play video games for one hour, the reason was that it lay in the variable playing time, namely one hour was considered too short [11]. There are advantages and disadvantages to video games for a person.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning on Game Addiction Detection Based on Electroencephalogram

Pangistu

Azhari

2021

mib

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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.

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“…In previous studies [7][8][9], the analysis was limited to frontal lobe in general. This distinguishes our study from other studies.…”

Section: Introductionmentioning

confidence: 99%

Disparity in brain dynamics for video games played on small and large displays

Malik

Pauzi

Osman

et al. 2012

2012 IEEE Symposium on Humanities, Science and Engineering Research

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In this research, we investigated the differences in brain activity while the subjects played video game. Experiment was conducted with two screen sizes; a large screen (40-inch display) and a small screen (3.5-inch handheld game). A total of 28 subjects participated in the study where two groups of 14 subjects played video games on two different screen sizes. Prior to video game play, baseline electroencephalogram (EEG) data were recorded during eyes open (EO) resting condition for five minutes. While the subjects played video game, the data were collected using a 19channel EEG machine. The analysis was made by comparing the EEG results of subjects playing video game with that of baseline data. In order to remove artefacts like eye blinking and eye movements, a pre-processing like filtering was performed to clean the data. The analysis was made by using three measures, i.e., absolute power, coherence and phase lag. The analysis was made on four main frequency bands. It was found that the brain was active in beta band as higher absolute power was observed in occipital, parietal frontal and motor regions while playing video game as compared to baseline condition. Also, the coherence results showed that these regions are well connected. Furthermore, faster communication was observed from phase lag result for these regions.

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Effect of the involvement degree of playing video games on brain waves for an hour

Cited by 7 publications

References 9 publications

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

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

Deep Learning on Game Addiction Detection Based on Electroencephalogram

Disparity in brain dynamics for video games played on small and large displays

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