Identifying neurophysiological correlates of stress
dc.contributor.author | Pei, Dingyi | |
dc.contributor.author | Tirumala, Shravika | |
dc.contributor.author | Tun, Kyaw T. | |
dc.contributor.author | Ajendla, Akshara | |
dc.contributor.author | Vinjamuri, Ramana | |
dc.date.accessioned | 2024-11-14T15:19:06Z | |
dc.date.available | 2024-11-14T15:19:06Z | |
dc.date.issued | 2024-10-24 | |
dc.description.abstract | Stress 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. | |
dc.description.sponsorship | The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was funded by National Science Foundation (NSF) CAREER Award, grant number HCC-2053498 and NSF IUCRC BRAIN CNS-2333292. | |
dc.description.uri | https://www.frontiersin.org/journals/medical-engineering/articles/10.3389/fmede.2024.1434753/full | |
dc.format.extent | 15 pages | |
dc.genre | journal articles | |
dc.identifier | doi:10.13016/m24qfh-bzcp | |
dc.identifier.citation | Pei, 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. | |
dc.identifier.uri | https://doi.org/10.3389/fmede.2024.1434753 | |
dc.identifier.uri | http://hdl.handle.net/11603/36991 | |
dc.language.iso | en_US | |
dc.publisher | frontiers | |
dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
dc.relation.ispartof | UMBC Faculty Collection | |
dc.relation.ispartof | UMBC Student Collection | |
dc.relation.ispartof | UMBC Center for Accelerated Real Time Analysis | |
dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department | |
dc.rights | Attribution 4.0 International CC BY 4.0 Deed | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.subject | EEG | |
dc.subject | mental stress | |
dc.subject | physiological signals | |
dc.subject | social stress | |
dc.subject | Stress detection | |
dc.subject | multi-class stress | |
dc.subject | multi-level stress | |
dc.title | Identifying neurophysiological correlates of stress | |
dc.type | Text | |
dcterms.creator | https://orcid.org/0009-0007-1757-5002 | |
dcterms.creator | https://orcid.org/0000-0001-7756-3678 | |
dcterms.creator | https://orcid.org/0000-0003-1650-5524 |
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