The Neurological Nexus: Exploring EEG, Facial Recognition, and Graph Algorithms in Mental Health AI

Abstract

The spread of depression anxiety and schizophrenia as mental health disorders creates demanding diagnostic and therapeutic hurdles for healthcare providers. Conventional patient examination requires patient words and doctor observation next to being slow and feeling dependent. New artificial intelligence technology helps doctors make better mental health diagnoses. This research studies how combining EEG readings with facial detection technology and graph analysis helps doctors better recognize mental health problems and treat them. EEG machines measure brain activity whereas facial recognition tools study emotional reactions to predict psychological states. Graph algorithms help us understand and interpret connections between neurological measurements. This work studies how different technologies work together and what computer processing problems they bring alongside ethical risks during mental health programs. By bringing these tools together we aim to improve disease detection methods and support both immediate medical tracking plus individual treatment programs.