Stress Analysis and Prediction using Machine Learning with EEG
Abstract
Stress, which is known to be the underlying cause of many of mental health disorders, arises from various of sources that have a clearly harmful effects on health. The consequences of stress are most noticeable in the life of a working professional who must handle the demands of increased management expectations, time management limitations, and family obligations. Proactive stress management is crucial since ignoring stress for a long time increases the likelihood of developing anxiety and depression.
Physiological characteristic is very essential for diagnosing stress-related conditions and provide important information about the complex relationship between mental and physical health. The goal of this study is to investigate stress using EEG data, which is recognized for its reliability, accuracy, and precision. Advanced (ML) machine learning models, such as SVM, RF, Decision Tree (DT), and KNN, are implemented based on the intrinsic compatibility between stress signals and EEG data.