Pornography Addiction Detection based on Neurophysiological Computational Approach

Abstract

<span lang="EN">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.</span>

Detecting learning disabilities based on neuro-physiological interface of affect (NPIoA)

“…Based on the experimental result for both positive and negative emotion, Subject A and B yielded u pattern. Therefore, it can be concluded that children with ASD shows the consistency lower alpha power as supported by previous studies on the reducing of alpha power for ASD cases [5,10].…”

“…The frequencies implemented in this research study are delta (1-3Hz), theta (4-8Hz), alpha (9-13Hz), beta (14-25Hz) and also gamma (26Hz-40Hz). The processed EEG data were then used to extract features based on the Mel Frequency Cepstral Coefficients (MFCCs) technique which has been applied in several studies for brain signals analysis [8][9][10]. After features were extracted, at each instance target was labelled based on the emotions data used indicating positive emotion calm with labelled (1, -1) and for negative emotion (fear) with labelled (-1, 1) for valence and arousal (V, A) respectively.…”

Neuro-physiological porn addiction detection using machine learning approach

“…Such scenario can pose as a challenge especially if parents have the tendency to suppress their children situation [15]. There has been studies to analyse porn addiction using the brain wave patterns [16,17]. However, such study only considers the power distribution of the frontal areas during the eyes open and eyes close and comparison with learning disabilities [18].…”

“…The data collection was prepared by the International Islamic University Malaysia (IIUM) researchers using the electroencephalogram (EEG) device for 14 Indonesian teenagers [17]. There are a total of 14 participants with age ranging between 9 to 13 years old EEG data was collected but only 11 participants data were used in this study.…”

“…The MFCC features are widely used in extracting relevant features in speech that approximates the human auditory system. It had been used in previous studies [17,18]…”