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

Authors

  • Nahid Neoaz Wilmington University, USA Author
  • Mohammad Hasan Amin Kettering University, Michigan Author

DOI:

https://doi.org/10.70445/gtst.1.1.2025.30-39

Keywords:

Depression Detection, Artificial Intelligence, Facial Recognition, EEG, Algorithmic Optimization, Multimodal Data Fusion, Mental Health Diagnostics

Abstract

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.

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Published

2025-01-25

Issue

Vol. 1 No. 1 (2025): Global Trends in Science and Technology

Section

Articles