Author(s) :

Khushee Goel.

Volume/Issue :

Abstract :

As smartphones became readily accessible to teenagers in 2010, longstanding trends for anxiety and depression skyrocketed. While statistics indicate that social media addiction is to blame, others argue that social media is more good than bad. This bears the question of how to distinguish between the productive and harmful use of social media. Employing EEG (electroencephalography) to measure brain activity, this research aims to identify biomarkers for internet addiction disorder (IAD). This study employed an open source Kaggle dataset of behavioral addiction disorders, with feature selection conducted through the Random Forest algorithm. This process led to the identification of 15 EEG biomarkers for addiction disorder, and then Odds Ratio analysis was used to identify the most significant ones. T-tests were conducted, beta coefficients were extracted, and logistic regression was used to further validate the findings. The discovered biomarkers now fill the void for quantitative measures of monitoring brain health upon social media consumption.