Profiling of pornography addiction among children using EEG signals: A systematic literature review

Xiaoxi Kang; Dini Oktarina Dwi Handayani; Pei Pei Chong; U Rajendra Acharya

doi:10.1016/j.compbiomed.2020.103970

Profiling of pornography addiction among children using EEG signals: A systematic literature review

Comput Biol Med. 2020 Oct:125:103970. doi: 10.1016/j.compbiomed.2020.103970. Epub 2020 Aug 18.

Authors

Xiaoxi Kang¹, Dini Oktarina Dwi Handayani², Pei Pei Chong³, U Rajendra Acharya⁴

Affiliations

¹ Master of Computer Science, Taylor's University, 1, Jalan Taylors, 47500, Subang Jaya, Selangor, Malaysia. Electronic address: kangxiaoxi@sd.taylors.edu.my.
² School of Computer Science & Engineering, Faculty of Innovation & Technology, Taylor's University, 1, Jalan Taylors, 47500, Subang Jaya, Selangor, Malaysia. Electronic address: dinioktarina.dwihandayani@taylors.edu.my.
³ School of Biosciences, Faculty of Health and Medical Sciences, Taylor's University, 1 Jalan Taylors, 47500, Subang Jaya, Selangor, Malaysia. Electronic address: peipei.chong@taylors.edu.my.
⁴ Ngee Ann, Singapore University of Social Science, University of Malaya, Malaysia; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan. Electronic address: aru@np.edu.sg.

PMID: 32892114
DOI: 10.1016/j.compbiomed.2020.103970

Abstract

Nowadays human behavior has been affected with the advent of new digital technologies. Due to the rampant use of the Internet by children, many have been addicted to pornography. This addiction has negatively affected the behaviors of children including increased impulsiveness, learning ability to attention, poor decision-making, memory problems, and deficit in emotion regulation. The children with porn addiction can be identified by parents and medical practitioners as third-party observers. This systematic literature review (SLR) is conducted to increase the understanding of porn addiction using electroencephalogram (EEG) signals. We have searched five different databases namely IEEE, ACM, Science Direct, Springer and National Center for Biotechnology Information (NCBI) using addiction, porn, and EEG as keywords along with 'OR 'operation in between the expressions. We have selected 46 studies in this work by screening 815,554 papers from five databases. Our results show that it is possible to identify children with porn addiction using EEG signals.

Keywords: Addiction; EEG; Pornography; Systematic literature review.

Publication types

Research Support, Non-U.S. Gov't
Review
Systematic Review

MeSH terms

Behavior, Addictive* / diagnosis
Child
Electroencephalography
Erotica*
Humans