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dc.contributor.authorPérez Valero, Eduardo 
dc.contributor.authorVaquero Blasco, Miguel Ángel 
dc.contributor.authorLópez Gordo, Miguel Ángel 
dc.date.accessioned2021-10-11T10:54:04Z
dc.date.available2021-10-11T10:54:04Z
dc.date.issued2021-08
dc.identifier.citationE. Perez-Valero et al. EEG-based multi-level stress classification with and without smoothing filter. Biomedical Signal Processing and Control 69 (2021) 102881. [https://doi.org/10.1016/j.bspc.2021.102881]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/70781
dc.descriptionThis work was supported by the project PGC2018-098813-B-C31 (the Spanish Ministry of Science, Innovation and Universities, by European Regional Development Funds and by the Nicolo Association for the R&D in Neurotechnology for disability. The authors would also like to thank Dr. Maria Jose Sanchez Carrion from the School for Special Education San Rafael of Granada, Hospitaller Order of St. John of God, for their support and for providing access to their chromotherapy room. We also want to thank Prof. Alexander Bertrand, from the Department of Electrical Engineering (ESAT) , Stadius Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, (Belgium) for his suggestions and valuable comments for this manuscript.es_ES
dc.description.abstractRecently, multi-level stress assessment has become an active research subject. In this context, researchers typically develop models based on machine learning classifiers and features extracted from biosignals like electrocardiogram (ECG) or electroencephalogram (EEG). For that purpose, EEG power spectral density (PSD) is a recurrent feature owing to its high responsiveness and remarkable performance. However, PSD is usually smoothed to cope with its bursty nature, what may cause data leakage and hence call into question classification performance. In this study, our aim was twofold: first, to examine the effect of EEG-PSD smoothing in three-level stress classification, and second, to evaluate the practical viability of a two-level stress detector without smoothing. To this end, we conducted participants through a stress-relax session while recording their EEG. Then, we estimated the EEG-PSD and used the stress reported by the participants as labels for classification. Initially, we developed a three-level stress classifier and examined the effect of smoothing on its performance. We found that classification performance was directly proportional to smoothing intensity (F1-score 0.61-0.94), and also that when smoothing was not applied to features, classification performance was insufficient for practical applicability (AUC < 0.7). We link this behavior to train-test contamination due to smoothing. Subsequently, we attempted two-level stress classification without smoothing. In this case, performance met the criteria for practical applicability (AUC = 0.76). This suggest that performance enhancement in three-level stress classification was caused by data leakage produced by smoothing, and hence, to render realistic stress classifiers each epoch should be processed individually.es_ES
dc.description.sponsorshipSpanish Ministry of Science, Innovation and Universities PGC2018-098813-B-C31es_ES
dc.description.sponsorshipEuropean Commissiones_ES
dc.description.sponsorshipNicolo Associationes_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectStress es_ES
dc.subjectClassificationses_ES
dc.subjectEEGes_ES
dc.subjectSignal smoothinges_ES
dc.subjectData leakagees_ES
dc.titleEEG-based multi-level stress classification with and without smoothing filteres_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1016/j.bspc.2021.102881
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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