Stress Detection Using EEG Signals: A Review on Current Strategies and Future Aspects

Stress, a constant in modern life, harms mental and physical health. Electroencephalography (EEG) is useful for monitoring cerebral activity and identifying stress neural correlates because it is non-invasive. This review discusses EEG-based stress detection methodologies and future directions. The review begins with stress's physiological cause and EEG manifestation. It then discusses stress detection preprocessing, feature extraction, and machine learning algorithms. These methods include spectrum analysis, time-domain features, and deep learning models like CNN's, RNN's, and hybrid models. Stress detection systems benefit from data quality, artifact reduction, and feature selection. The paper also discusses EEG based stress detection in clinical diagnosis, occupational stress evaluation, and Brain-Machine Interface. It discusses real-world deployment issues and wearable EEG devices for continuous stress monitoring. Going ahead, the paper covers promising EEG stress detection technologies. This includes integrating multi modal data sources including physiological signals, eye-tracking, and behavioral data, creating tailored stress profiles, and using sophisticated machine learning and deep learning methods like transfer learning and AI.