Machine Learning, Physiological Signals, and Emotional Stress/Anxiety: Pitfalls and Challenges

Anxiety and emotional stress are pervasive psychological challenges that profoundly impact human health in today’s fast-paced society. Traditional assessment methods, such as self-reports and clinical interviews, often suffer from subjective biases and lack the capability for objective, real-time evaluation of mental states. However, the integration of physiological signals—including electroencephalography (EEG), heart rate (HR), electrodermal activity (EDA), and eye movements—with advanced machine learning (ML) techniques, offers a promising approach to automate and objectify mental health assessments. A systematic review was conducted to explore recent advances in the early detection of anxiety and stress by combining physiological signals and ML methods. To assess methodological quality, a specific analysis framework was designed for the 113 studies included, which identified significant deficiencies in the literature. This highlights the urgent need to adopt standardized reporting guidelines in this field. The role of these technologies in feature extraction, classification, and predictive modeling was analyzed, also addressing critical challenges related to data quality, model interpretability, and the influence of intersectional factors like gender and age. Ethical and privacy considerations in the current research were also included. Finally, potential avenues for future research were summarized, highlighting the potential of ML technologies for early detection and proactive intervention in mental disorders.