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dc.contributor.authorAlonso López, Joan Francesc
dc.contributor.authorRomero Lafuente, Sergio
dc.contributor.authorBallester Verneda, Maria Rosa
dc.contributor.authorAntonijoan Arbós, Rosa Maria
dc.contributor.authorMañanas Villanueva, Miguel Ángel
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.identifier.citationAlonso, J.F. [et al.]. Stress assessment based on EEG univariate features and functional connectivity measures. "Physiological measurement", 27 Maig 2015, vol. 36, p. 1351-1365.
dc.description.abstractThe biological response to stress originates in the brain but involves different biochemical and physiological effects. Many common clinical methods to assess stress are based on the presence of specific hormones and on features extracted from different signals, including electrocardiogram, blood pressure, skin temperature, or galvanic skin response. The aim of this paper was to assess stress using EEG-based variables obtained from univariate analysis and functional connectivity evaluation. Two different stressors, the Stroop test and sleep deprivation, were applied to 30 volunteers to find common EEG patterns related to stress effects. Results showed a decrease of the high alpha power (11 to 12 Hz), an increase in the high beta band (23 to 36 Hz, considered a busy brain indicator), and a decrease in the approximate entropy. Moreover, connectivity showed that the high beta coherence and the interhemispheric nonlinear couplings, measured by the cross mutual information function, increased significantly for both stressors, suggesting that useful stress indexes may be obtained from EEG-based features.
dc.format.extent15 p.
dc.publisherInstitute of Physics (IOP)
dc.subjectÀrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
dc.subject.otherStroop test
dc.subject.othersleep deprivation
dc.subject.otherspectral analysis
dc.subject.othermutual information function.
dc.titleStress assessment based on EEG univariate features and functional connectivity measures
dc.subject.lemacCervell -- Processament de dades
dc.contributor.groupUniversitat Politècnica de Catalunya. BIOART - BIOsignal Analysis for Rehabilitation and Therapy
dc.description.peerreviewedPeer Reviewed
dc.rights.accessOpen Access
dc.description.versionPostprint (author’s final draft)
local.citation.authorAlonso, J.F.; Romero, S.; Ballester, M. R.; Antonijoan, R. M.; Mañanas, M.A.
local.citation.publicationNamePhysiological measurement

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