A Machine Learning Approach for Multi-Level Anxiety Screening among University-Going Students using Wireless EEG Signals

Abstract

Anxiety is a widespread mental health condition affecting millions globally, often resulting in significant emotional and physical symptoms. Accurate detection of anxiety levels is essential to provide timely interventions and prevent severe complications. This study explores a machine learning-based approach for multilevel anxiety classification among young adults using EEG signals. The GAD-7 screening tool was used to assess and categorize participants into different anxiety severity groups. EEG data was then recorded, processed, and segmented into 1, 3, and 5 second segments to evaluate the impact of segment duration on classification accuracy. Four channel combinations were tested for comparisons in performance. Feature extraction included eleven time and frequency domain features. The Bagged Trees classifier was applied to classify anxiety levels based on these features. The findings of this work show the potential of EEG-based systems as non-invasive tools for anxiety screening that could support more precise mental health diagnostics.