Identifying neurophysiological correlates of stress

dc.contributor.authorPei, Dingyi
dc.contributor.authorTirumala, Shravika
dc.contributor.authorTun, Kyaw T.
dc.contributor.authorAjendla, Akshara
dc.contributor.authorVinjamuri, Ramana
dc.date.accessioned2024-11-14T15:19:06Z
dc.date.available2024-11-14T15:19:06Z
dc.date.issued2024-10-24
dc.description.abstractStress 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.sponsorshipThe 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.urihttps://www.frontiersin.org/journals/medical-engineering/articles/10.3389/fmede.2024.1434753/full
dc.format.extent15 pages
dc.genrejournal articles
dc.identifierdoi:10.13016/m24qfh-bzcp
dc.identifier.citationPei, 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.urihttps://doi.org/10.3389/fmede.2024.1434753
dc.identifier.urihttp://hdl.handle.net/11603/36991
dc.language.isoen_US
dc.publisherfrontiers
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Center for Accelerated Real Time Analysis
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department
dc.rightsAttribution 4.0 International CC BY 4.0 Deed
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectEEG
dc.subjectmental stress
dc.subjectphysiological signals
dc.subjectsocial stress
dc.subjectStress detection
dc.subjectmulti-class stress
dc.subjectmulti-level stress
dc.titleIdentifying neurophysiological correlates of stress
dc.typeText
dcterms.creatorhttps://orcid.org/0009-0007-1757-5002
dcterms.creatorhttps://orcid.org/0000-0001-7756-3678
dcterms.creatorhttps://orcid.org/0000-0003-1650-5524

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