A Multimodal Intelligent Framework for Real-Time Stress Detection and Personalized Mental Health Assessment Using EEG, Facial Expression Analysis, and Machine Learning

Authors: Shaikh Shorab

Country: India

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Abstract: Stress has become a major factor affecting human health and daily life, making timely detection and management essential for maintaining well-being. This paper presents a web-based intelligent system developed to identify stress levels using multiple data sources. The proposed platform collects Electroencephalogram (EEG) parameters entered by the user, captures facial expressions through a webcam, and evaluates responses to structured questionnaire-based questions. These inputs are processed using machine learning and computer vision techniques to determine the user’s stress level, which is categorized as low, medium, or high. After classification, the system generates a personalized report and provides suitable precautionary suggestions such as relaxation methods, breathing exercises, and motivational guidance to help users manage stress effectively. The platform also enables users to monitor their condition through recorded results, supporting better awareness and long-term stress management. The overall objective of the system is to offer an accessible and user-friendly solution that integrates multiple indicators to improve the accuracy of stress detection and promote mental health care.

Keywords: Stress Detection, EEG Parameters, Facial Expression Analysis, Machine Learning, Mental Health, Personalized Recommendations.

Paper Id: 233184

Published On: 2026-07-29

Published In: Volume 14, Issue 4, July-August 2026

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