top of page
Asian Institute of Research, Journal Publication, Journal Academics, Education Journal, Asian Institute
Asian Institute of Research, Journal Publication, Journal Academics, Education Journal, Asian Institute

Journal of Health and Medical Sciences

ISSN 2622-7258

Screen Shot 2018-08-12 at 1.24.09 AM.png
Screen Shot 2018-08-12 at 1.24.02 AM.png
Screen Shot 2018-08-12 at 1.23.57 AM.png
Screen Shot 2018-08-12 at 1.23.52 AM.png
crossref
doi
open access

Published: 29 November 2021

QEEG-based Brain Mapping of Internet Pornography Addicted Adolescents

Nur Amilah, Yayu Hizza Anisa, Mia Kamayani Sulaeman, Nita Handayani, Pukovisa Prawiroharjo, Rizki Edmi Edison

University of Muhammadiyah Prof Dr HAMKA (Indonesia), Brain Beta Lab (Indonesia), Sunan Kalijaga State Islamic University (Indonesia), University of Indonesia (Indonesia)

journal of social and political sciences
pdf download

Download Full-Text Pdf

doi

10.31014/aior.1994.04.04.195

Pages: 67-72

Keywords: QEEG, Addiction, Pornography, Teens

Abstract

The Indonesian government for many years has tried to protect the public from the dangers of pornography by blocking various sites. Although various efforts have been made to block access to pornography, a report from the Ministry of Women's Empowerment and Child Protection mentioned that 97% of Indonesian teens were exposed to pornography from the internet. In order to increase awareness, especially in the addiction phase, scientific evidences showing the bad effects of pornography addiction is needed. In this study, 15 teens addicted to internet pornography underwent brain mapping using electroencephalography (EEG) in a resting state for approximately 20 minutes. The data were processed using a quantitative EEG (QEEG) approach, especially Fast Fourrier Transform (FFT) by first removing all artifacts on the electroencephalogram during recording. The analysis focused on the delta wave in the forebrain, showing the dominance of the prefrontal cortex, which has implications for cognitive function decline, especially the braking system among these teens addicted to internet pornography. The decline in cognitive function causes teens to lose the ability to determine what is right and wrong or refrain from doing wrong. Based on the results, efforts to educate teens about the dangers of pornography addiction need to be further promoted.

References

  1. Hilton, D., & Watts, C. (2011). Pornography addiction: A neuroscience perspective. Surgical Neurology International, 2(1). https://doi.org/10.4103/2152-7806.76977

  2. Hou, J., Jiang, Y., Chen, S., Hou, Y., Wu, J., Fan, N., & Fang, X. (2019). Cognitive mechanism of intimate interpersonal relationships and loneliness in internet-addicts: An ERP study. Addictive Behaviors Reports, 10(May), 100209. https://doi.org/10.1016/j.abrep.2019.100209

  3. Kamaruddin, N., Wahab, A., & Rozaidi, Y. (2019). Neuro-Physiological porn addiction detection using machine learning approach. Indonesian Journal of Electrical Engineering and Computer Science, 16(2), 964–971. https://doi.org/10.11591/ijeecs.v16.i2.pp964-971

  4. Kemenpppa. (2018). Press Release: Cegah Anak Terpapar Narkoba dan Pornografi Sejak Dini. www.kemenpppa.go.id

  5. Kemkominfo. (2012). Sebanyak 835 Ribu Situs Porno telah Terblokir sejak 2010. www.kominfo.go.id

  6. Kemkominfo. (2018). Press Release: Cegah Anak Terpapar Narkoba dan Pornografi Sejak Dini. www.kemenpppa.go.id

  7. Lestari, F. P., Haekal, M., Edmi Edison, R., Ravi Fauzy, F., Nurul Khotimah, S., & Haryanto, F. (2020). Epileptic Seizure Detection in EEGs by Using Random Tree Forest, Naïve Bayes and KNN Classification. Journal of Physics: Conference Series, 1505(1). https://doi.org/10.1088/1742-6596/1505/1/012055

  8. Levashina, J., & Campion, M. A. (2007). Measuring Faking in the Employment Interview: Development and Validation of an Interview Faking Behavior Scale. Journal of Applied Psychology, 92(6), 1638–1656. https://doi.org/10.1037/0021-9010.92.6.1638

  9. Love, T., Laier, C., Brand, M., Hatch, L., & Hajela, R. (2015). Neuroscience of internet pornography addiction: A review and update. Behavioral Sciences,5(3), 388–433. https://doi.org/10.3390/bs5030388

  10. Malki, K., Rahm, C., Öberg, K. G., & Ueda, P. (2021). Frequency of Pornography Use and Sexual Health Outcomes in Sweden: Analysis of a National Probability Survey. Journal of Sexual Medicine. https://doi.org/10.1016/j.jsxm.2021.08.003

  11. Prawiroharjo, P., Ellydar, H., Pratama, P., Edison, R. E., Suaidy, S. E. I., Amani, N. Z., & Carissima, D. (2019). Impaired Recent Verbal Memory in Pornography-Addicted Juvenile Subjects. Neurology Research International,2019. https://doi.org/10.1155/2019/2351638

  12. Sengoku, A., & Takagi, S. (1998). Electroencephalographic findings in functional psychoses: State or trait indicators? Psychiatry and Clinical Neurosciences,52(4), 375–381. https://doi.org/10.1046/j.1440-1819.1998.00414.x

  13. Shah, A. K., & Mittal, S. (2014). Evaluation of magnetic resonance imaging-negative drug-resistant epilepsy. Annals of Indian Academy of Neurology, 17(SUPPL. 1). https://doi.org/10.4103/0972-2327.128667

  14. Siuly, S., Li, Y., & Zhang, Y. (2016). EEG Signal Analysis and Classification Techniques and Applications. In Springer. http://www.springer.com/series/11944

  15. Strumwasser, F. (1994). the Relations Between Neuroscience and Human Behavioral Science. Journal of the Experimental Analysis of Behavior, 61(2), 307–317. https://doi.org/10.1901/jeab.1994.61-307

  16. Wang, Y., Huang, Z., McCane, B., & Neo, P. (2018). EmotioNet: A 3-D Convolutional Neural Network for EEG-based Emotion Recognition. Proceedings of the International Joint Conference on Neural Networks, 2018-July, 1–7. https://doi.org/10.1109/IJCNN.2018.8489715

  17. Wilner Warren. (1987). Participatory Experience : The Participatory Observer Paradox. https://doi.org/https://doi.org/10.1007/BF01255227

  18. Zhou, M., Tian, C., Cao, R., Wang, B., Niu, Y., Hu, T., Guo, H., & Xiang, J. (2018). Epileptic seizure detection based on EEG signals and CNN. Frontiers in Neuroinformatics, 12(December), 1–14. https://doi.org/10.3389/fninf.2018.00095

bottom of page