An IoT Enabled EEG Monitoring System for Real-Time Visualization and Analysis of Stress Levels

Abstract

Chronic stress is a primary catalyst for global neurological decline and cardiovascular morbidity, requiring continuous physiological surveillance to prevent long-term health deterioration. Despite the precision of modern electroencephalography (EEG), current monitoring systems frequently fail to achieve real-world clinical utility due to the restricted real-time accessibility of data and the lack of intuitive visualization tools for remote practitioners. This study proposes an IoT-enabled EEG monitoring system designed for the real-time visualization and analysis of patient stress levels. The system architecture utilizes a PHP-MySQL backend and a centralized web dashboard to facilitate immediate clinical feedback. Following an Agile development methodology, the system underwent iterative refinement through six distinct phases, ensuring the alignment of the IoT gateway with clinical requirements. Experimental validation yielded a classification accuracy of 82% and an average system response time of 1.8 seconds. While this latency includes the necessary overhead for IoT network transmission and database handshaking, the results demonstrate a viable balance between computational precision and remote accessibility. The system provides a scalable solution for psychiatric intervention, contributing to the advancement of personalized digital health and demonstrating high utility for proactive stress management in outpatient settings.