From Theory to Implementation: Optimizing AI-Driven Depression Detection Using Facial Recognition, EEG, and Algorithmic Innovations

Our research demonstrates the power of artificial intelligence to identify depression through improved detection of facial features and brain wave patterns. The exploration builds on current algorithm advances to develop depression detection methods that work accurately and quickly. This project examines AI's theoretical framework for mental health diagnosis plus reviews modern methods and studies practical uses and computational issues.