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.
The electroencephalogram (EEG) signal data were obtained from Yayasan Kita dan Buah Hati (YKBH), Jakarta, Indonesia and collected using a Brain Maker EEG machine with 19 channels. The sampling rate of the machine was 250 Hz. Fourteen participants (five females and nine males) participated in the data collection. A psychologist verified that seven of them were addicted to porn, and seven were healthy teenagers. The EEG data were recorded using one protocol with nine tasks for 10 min. The three stages were the baseline (tasks with eyes closed and open), emotional state (happy, calm, sad and fearful tasks) and main (15-words memorisation task, executive task and 15-words recall task) stages. The data obtained was used to analyse the signal pattern of pornography addiction amongst teenagers, as well as the emotional signal pattern and working memory capacity.