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.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Deep Learning on Game Addiction Detection Based on Electroencephalogram


    Beteiligte:

    Erscheinungsdatum :

    2021-07-31


    Anmerkungen:

    doi:10.30865/mib.v5i3.3061
    JURNAL MEDIA INFORMATIKA BUDIDARMA; Vol 5, No 3 (2021): Juli 2021; 963-970 ; 2548-8368 ; 2614-5278 ; 10.30865/mib.v5i3



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629




    Betel Nut Addiction Detection Using Machine Learning

    Khatun, Johura / Azad, Tamanna / Seaum, Samin Yeasar et al. | IEEE | 2020


    Electroencephalogram and Aerospace Safety

    W. H. King / E. Liske | NTIS | 1973



    Electroencephalogram-based control of a mobile robot

    Tanaka, Kazuo | Online Contents | 2005


    Driver fatigue detection from electroencephalogram spectrum after electrooculography artefact removal

    Hu, Shuyan / Zheng, Gangtie / Peters, Björn | Wiley | 2013

    Freier Zugriff