Pei, DingyiTirumala, ShravikaTun, Kyaw T.Ajendla, AksharaVinjamuri, Ramana2024-11-142024-11-142024-10-24Pei, Dingyi, Shravika Tirumala, Kyaw T. Tun, Akshara Ajendla, and Ramana Vinjamuri. “Identifying Neurophysiological Correlates of Stress.” Frontiers in Medical Engineering 2 (October 24, 2024). https://doi.org/10.3389/fmede.2024.1434753.https://doi.org/10.3389/fmede.2024.1434753http://hdl.handle.net/11603/36991Stress has been recognized as a pivotal indicator which can lead to severe mental disorders. Persistent exposure to stress will increase the risk for various physical and mental health problems. Early and reliable detection of stress-related status is critical for promoting wellbeing and developing effective interventions. This study attempted multi-type and multi-level stress detection by fusing features extracted from multiple physiological signals including electroencephalography (EEG) and peripheral physiological signals. Eleven healthy individuals participated in validated stress-inducing protocols designed to induce social and mental stress and discriminant multi-level and multi-type stress. A range of machine learning methods were applied and evaluated on physiological signals of various durations. An average accuracy of 98.1% and 97.8% was achieved in identifying stress type and stress level respectively, using 4-s neurophysiological signals. These findings have promising implications for enhancing the precision and practicality of real-time stress monitoring applications.15 pagesen-USAttribution 4.0 International CC BY 4.0 Deedhttps://creativecommons.org/licenses/by/4.0/EEGmental stressphysiological signalssocial stressStress detectionmulti-class stressmulti-level stressText