Deep Learning on Game Addiction Detection Based on Electroencephalogram
DOI:
https://doi.org/10.30865/mib.v5i3.3061Keywords:
Electroencephalogram, Game Addiction, Fast Fourier Transform, Deep Learning, Convolutional Neural NetworkAbstract
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.Â
References
O. Király, K. Nagygyörgy, M. D. Griffiths, and Z. Demetrovics, Problematic Online Gaming. 2014.
D. Bavelier and R. J. Davidson, “Games to do you good,†Nature, vol. 494, no. 7438, pp. 425–426, 2013.
M. Griffiths, “Does Internet and computer ‘addiction’ exist? Some case study evidence,†Cyberpsychology Behav., vol. 3, no. 2, pp. 211–218, 2000.
C. L. Mathews, H. E. R. Morrell, and J. E. Molle, “Video game addiction, ADHD symptomatology, and video game reinforcement,†Am. J. Drug Alcohol Abuse, vol. 45, no. 1, pp. 67–76, 2019.
J. E. Brand, S. Todhunter, and J. Jervis, “Digital Australia 2018,†p. 43, 2017.
Q. Wu and J. Carette, “Can Deep Learning Predict Problematic Gaming?,†IEEE Conf. Comput. Intell. Games, CIG, vol. 2020-Augus, pp. 662–665, 2020.
E. Chang and B. Kim, “School and individual factors on game addiction: A multilevel analysis,†Int. J. Psychol., vol. 55, no. 5, pp. 822–831, 2020.
B. L. M. Adams, V. Stavropoulos, T. L. Burleigh, L. W. L. Liew, C. L. Beard, and M. D. Griffiths, “Internet Gaming Disorder Behaviors in Emergent Adulthood: a Pilot Study Examining the Interplay Between Anxiety and Family Cohesion,†Int. J. Ment. Health Addict., vol. 17, no. 4, pp. 828–844, 2019.
T. L. Burleigh, V. Stavropoulos, L. W. L. Liew, B. L. M. Adams, and M. D. Griffiths, “Depression, Internet Gaming Disorder, and the Moderating Effect of the Gamer-Avatar Relationship: an Exploratory Longitudinal Study,†Int. J. Ment. Health Addict., vol. 16, no. 1, pp. 102–124, 2018.
D. J. Kim et al., “Internet Game Addiction, Depression, and Escape from Negative Emotions in Adulthood: A Nationwide Community Sample of Korea,†J. Nerv. Ment. Dis., vol. 205, no. 7, pp. 568–573, 2017.
F. L. Lin, C. L. Chang, Y. T. Jou, S. C. Pan, T. Y. Hsu, and C. D. Huang, “Effect of the involvement degree of playing video games on brain waves for an hour,†Proc. - 2010 IEEE 17th Int. Conf. Ind. Eng. Eng. Manag. IE EM2010, pp. 1043–1047, 2010.
M. Mustafa, R. A. Mustafar, R. Samada, N. R. H. Abdullah, and N. Sulaiman, “OBSERVATION OF THE EFFECTS OF PLAYING GAMES WITH THE HUMAN BRAIN WAVES,†J. Teknol. (Sciences Eng., vol. 7, pp. 61–65, 2015.
H. Azmy and N. M. Safri, “EEG based BCI using visual imagery task for robot control,†J. Teknol. (Sciences Eng., vol. 61, no. 2 SUPPL, pp. 7–11, 2013.
V. A. Maksimenko et al., “Artificial neural network classification of motor-related EEG: An increase in classification accuracy by reducing signal complexity,†Complexity, vol. 2018, 2018.
E. C. Djamal, Suprijanto, and A. Arif, “Identification of alertness state based on EEG signal using wavelet extraction and neural networks,†Proceeding - 2014 Int. Conf. Comput. Control. Informatics Its Appl. “New Challenges Oppor. Big Dataâ€, IC3INA 2014, pp. 176–180, 2014.
Z. Guo, Y. Pan, G. Zhao, S. Cao, and J. Zhang, “Detection of Driver Vigilance Level Using EEG Signals and Driving Contexts,†IEEE Trans. Reliab., vol. 67, no. 1, pp. 370–380, 2018.
M. Li, H. Xu, X. Liu, and S. Lu, “Emotion recognition from multichannel EEG signals using K-nearest neighbor classification,†Technol. Heal. Care, vol. 26, no. S1, pp. S509–S519, 2018.
K. Guo, H. Candra, H. Yu, H. Li, H. T. Nguyen, and S. W. Su, “EEG-based emotion classification using innovative features and combined SVM and HMM classifier,†Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, pp. 489–492, 2017.
F. H. Duffy and H. Als, “A stable pattern of EEG spectral coherence distinguishes children with autism from neuro-typical controls - a large case control study,†BMC Med., vol. 10, no. June, 2012.
S. Ibrahim, R. Djemal, and A. Alsuwailem, “Electroencephalography (EEG) signal processing for epilepsy and autism spectrum disorder diagnosis,†Biocybern. Biomed. Eng., vol. 38, no. 1, pp. 16–26, 2018.
M. K. Kiymik, M. Akin, and A. Subasi, “Automatic recognition of alertness level by using wavelet transform and artificial neural network,†J. Neurosci. Methods, vol. 139, no. 2, pp. 231–240, 2004.
N. Koolen et al., “Automated classification of neonatal sleep states using EEG,†Clin. Neurophysiol., vol. 128, no. 6, pp. 1100–1108, 2017.
A. R. Hassan and M. I. H. Bhuiyan, “Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting,†Comput. Methods Programs Biomed., vol. 140, pp. 201–210, 2017.
M. Zhou et al., “Epileptic seizure detection based on EEG signals and CNN,†Front. Neuroinform., vol. 12, no. December, pp. 1–14, 2018.
L. Ghosh, S. Saha, and A. Konar, “Decoding emotional changes of android-gamers using a fused Type-2 fuzzy deep neural network,†Comput. Human Behav., vol. 116, no. November 2020, p. 106640, 2021.
N. Kamaruddin, A. Wahab, and Y. Rozaidi, “Neuro-Physiological porn addiction detection using machine learning approach,†Indones. J. Electr. Eng. Comput. Sci., vol. 16, no. 2, pp. 964–971, 2019.
H. Zeng, C. Yang, G. Dai, F. Qin, J. Zhang, and W. Kong, “EEG classification of driver mental states by deep learning,†Cogn. Neurodyn., vol. 12, no. 6, pp. 597–606, 2018.
E. C. Djamal, W. I. Furi, and F. Nugraha, “Detection of EEG Signal Post-Stroke Using FFT and Convolutional Neural Network,†Proceeding Electr. Eng. Comput. Sci. Informatics, vol. 6, no. 1, pp. 18–23, 2019.
U. Wong and D. C. Hodgins, “Development of the game addiction inventory for adults (GAIA),†Addict. Res. Theory, vol. 22, no. 3, pp. 195–209, 2014.
M. Dai, D. Zheng, R. Na, S. Wang, and S. Zhang, “EEG classification of motor imagery using a novel deep learning framework,†Sensors (Switzerland), vol. 19, no. 3, pp. 1–16, 2019.
M. G. Ragab et al., “A novel one-dimensional cnn with exponential adaptive gradients for air pollution index prediction,†Sustain., vol. 12, no. 23, pp. 1–22, 2020.
H. K. Lee and Y. S. Choi, “A convolution neural networks scheme for classification of motor imagery EEG based on wavelet time-frequecy image,†Int. Conf. Inf. Netw., vol. 2018-Janua, pp. 906–909, 2018.
Y. Di, X. An, S. Liu, F. He, and D. Ming, “Using Convolutional Neural Networks for Identification Based on EEG Signals,†Proc. - 2018 10th Int. Conf. Intell. Human-Machine Syst. Cybern. IHMSC 2018, vol. 2, pp. 119–122, 2018.
G. C. Jana, R. Sharma, and A. Agrawal, “A 1D-CNN-Spectrogram Based Approach for Seizure Detection from EEG Signal,†Procedia Comput. Sci., vol. 167, no. 2019, pp. 403–412, 2020.
A. Qayyum, M. K. A. A. Khan, M. Mazher, and M. Suresh, “Classification of EEG Learning and Resting States using 1D-Convolutional Neural Network for Cognitive Load Assesment,†2018 IEEE 16th Student Conf. Res. Dev. SCOReD 2018, pp. 1–5, 2018.
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