AI-Enhanced Depression and Anxiety Detection: Integrating EEG Systems, Performance-Cost Trade-Offs, and Optimization Algorithms

Abstract

Millions of people worldwide now experience depression and anxiety as primary mental health conditions. The early detection of mental health issues lets us stop their continued deterioration. Modern medical practitioners choose EEG as their preferred noninvasive brain activity assessment tool which helps detect mental health disorder patterns. The research presents a new EEG-based sadness and anxiety detection system which integrates artificial intelligence technology. Through our study we built a real-time mental health detection system that unites system functioning with optimal solutions to deliver efficient diagnostic results immediately. We integrated advanced machine learning technologies with EEG data to build a framework that enhances both the affordability and accessibility of detection processes across multiple medical settings.