AI-Driven Mental Health Detection: Integrating Brain Signals, Facial Data, and Advanced Optimization Techniques

Abstract

The use of Artificial intelligence technology has revolutionized the methods we use to detect and handle mental health problems. This book studies how AI technologies detect mental health conditions including depression and anxiety at their most advanced level today. EEG detectors, facial recognition systems, and algorithms like the cat swarm algorithm form a complete mental healthcare framework according to this book. Researchers examine how the VPSYC AI platform affects personalized mental health support through real-time detection and treatment of depression. The book examines how AI works with EEG systems that track brain wave patterns to identify mental health conditions. This research compares how deep learning and traditional computer vision facial recognition systems work to detect emotional distress. The book evaluates optimization methods to find how the cat swarm algorithm might improve mental health evaluation processes.