Pornography Addiction Detection based on Neurophysiological Computational Approach

Received Jan 9, 2018 Revised Mar 2, 2018 Accepted Mar 18, 2018 The rise of Internet access, social media and availability of smart phones intensify the epidemic of pornography addiction especially among younger teenagers. Such scenario may offer many side effects to the individual such as alteration of the behavior, changes in moral value and rejection to normal community convention. Hence, it is imperative to detect pornography addiction as early as possible. In this paper, a method of using brain signal from frontal area captured using EEG is proposed to detect whether the participant may have porn addiction or otherwise. It acts as a complementary approach to common psychological questionnaire. Experimental results show that the addicted participants had low alpha waves activity in the frontal brain region compared to non-addicted participants. It can be observed using power spectra computed using Low Resolution Electromagnetic Tomography (LORETA). The theta band also show there is disparity between addicted and non-addicted. However, the distinction is not as obvious as alpha band. Subsequently, more work need to be conducted to further test the validity of the hypothesis. It is envisaged that with more participants and further investigation, the proposed method will be the initial step to groundbreaking way of understanding the way porn addiction affects the brain.