Correlation of learning disabilities to porn addiction based on EEG

Researchers were able to correlate porn addiction based on electroencephalogram (EEG) signal analysis to the psychological instruments’ findings. In this paper we attempt to correlate the porn addiction to various cases of learning disorders through analyzing EEG signals. Since porn addiction involv...

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Bibliographic Details
Published in:Bulletin of Electrical Engineering and Informatics
Main Author: Kamaruddin N.; Razi N.I.M.; Wahab A.
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science 2021
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091063150&doi=10.11591%2feei.v10i1.2462&partnerID=40&md5=7303ddb677f0cf7296132ec43d90ef93
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Summary:Researchers were able to correlate porn addiction based on electroencephalogram (EEG) signal analysis to the psychological instruments’ findings. In this paper we attempt to correlate the porn addiction to various cases of learning disorders through analyzing EEG signals. Since porn addiction involved the brainwave power at the frontal of the brain, which reflects the executive functions, this may have correlation to learning disorder. Only three types of learning disorder will be of interest in our study involving dyslexic, attention deficit and hyperactivity disorder (ADHD) and autistic children because they involved reduced intellectual ability observed from the lack of listening, speaking, reading, writing, reasoning, or mathematical proficiencies. Children with such disorder when expose to the internet unfiltered porn contents may have minimal understanding of the negative effects of the contents. Such unmonitored exposure to pornographic contents may result to porn addiction because it may trigger excitement and induced pleasure. Experimental results show strong correlation of learning disorders to porn addiction, which can be worthwhile for further analysis. In addition, this paper also indicates that analyzing brainwave patterns could provide a better insight into predicting and detecting children with learning disorders and addiction with direct analysis of the brain wave patterns. © 2020, Institute of Advanced Engineering and Science. All rights reserved.
ISSN:20893191
DOI:10.11591/eei.v10i1.2462