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@article{196625,
        author = {KOTRAKONA DIVYA TEJ and Mrs. J. Sivasangari and MUNAGALA MARUTHI PHANI MOHANA RAO and Maram Reddy Sai Teja},
        title = {Mental Stress Detection using Machine Learning Techniques with EEG},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {3821-3827},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196625},
        abstract = {In today's fast-paced society, stress has become a significant health concern affecting millions of individuals worldwide. This paper reviews machine learning-based techniques for detecting stress using physiological, behavioral, and contextual data. Various physiological signals such as heart rate variability, electrodermal activity, breathing rate, and EEG signals are analyzed using machine learning algorithms including Support Vector Machines, Random Forest, Logistic Regression, Decision Trees, and Neural Networks. Performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC are evaluated. The study highlights that Random Forest and deep learning-based approaches achieve higher accuracy levels in stress detection. This review emphasizes the importance of wearable technology and real-time monitoring systems in improving healthcare and mental well-being.},
        keywords = {Stress Detection, Machine Learning, Wearable Technology, Healthcare, Deep Learning, Physiological Signals},
        month = {April},
        }