Identifying EEG-based neurobehavioral risk markers of gaming addiction using machine learning and iowa gambling task

Internet Gaming Disorder (IGD), Gaming Disorder (GD), and Internet Addiction represent behavioral patterns with significant psychological and neurological consequences. Affected individuals often disengage from routine activities and exhibit distress upon interruption of gaming, impacting family life and overall well-being. Timely and objective detection methods are essential. This study investigates EEG-based biomarkers in healthy participants, aiming to classify them into two groups based on behavioral patterns observed during the Iowa Gambling Task (IGT). EEG and IGT data were collected simultaneously, with IGT serving as a cognitive challenge to induce decision-making under uncertainty and risk. EEG signals were segmented into event-related potentials (ERPs), pre-processed, and used to extract temporal features. Advanced signal transformation techniques, including Fast Fourier Transform (FFT), Power Spectral Density (PSD), Autocorrelation Function (ACF), and Wavelet Transforms, were employed to build the feature space. Machine Learning (ML) and Deep Learning (DL) classifiers, particularly Random Forest (RF) and Convolutional Neural Networks (CNN), were trained and validated, achieving a classification accuracy of 93%. This approach offers early detection of abnormal decision-making behavior and distinguishes participants based on neurophysiological responses, enhancing diagnostic speed and objectivity. The study emphasizes methodological transparency, ethical compliance, and data availability, providing a replicable framework for future investigations into behavioral biomarkers of gaming-related disorders.