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Automatic cognitive fatigue detection using wearable fNIRS and machine learning

2022Journal article Open access
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dc.contributor.authorVarandas, Rui
dc.contributor.authorLima, Rodrigo
dc.contributor.authorBermúdez i Badia, Sergi
dc.contributor.authorSilva, Hugo
dc.contributor.authorGamboa, Hugo
dc.date.accessioned2022-07-22T10:15:46Z
dc.date.available2022-07-22T10:15:46Z
dc.date.issued2022
dc.description.abstractWearable sensors have increasingly been applied in healthcare to generate data and monitor patients unobtrusively. Their application for Brain–Computer Interfaces (BCI) allows for unobtru sively monitoring one’s cognitive state over time. A particular state relevant in multiple domains is cognitive fatigue, which may impact performance and attention, among other capabilities. The monitoring of this state will be applied in real learning settings to detect and advise on effective break periods. In this study, two functional near-infrared spectroscopy (fNIRS) wearable devices were employed to build a BCI to automatically detect the state of cognitive fatigue using machine learning algorithms. An experimental procedure was developed to effectively induce cognitive fatigue that included a close-to-real digital lesson and two standard cognitive tasks: Corsi-Block task and a concentration task. Machine learning models were user-tuned to account for the individual dynamics of each participant, reaching classification accuracy scores of around 70.91 ± 13.67%. We concluded that, although effective for some subjects, the methodology needs to be individually validated before being applied. Moreover, time on task was not a particularly determining factor for classification, i.e., to induce cognitive fatigue. Further research will include other physiological signals and human–computer interaction variables.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationVarandas, R., Lima, R., Badia, S. B. I., Silva, H., & Gamboa, H. (2022). Automatic cognitive fatigue detection using wearable fNIRS and machine learning. Sensors, 22(11), 4010.pt_PT
dc.identifier.doi10.3390/s22114010pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/4450
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationBiosignals Training Methods based on Biosignals Monitoring
dc.relationEmotional Regulation Assessment via multi-biosignal processing in a VR environment for neurorehabilitation.
dc.relationNOVA Laboratory for Computer Science and Informatics
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectCognitive fatiguept_PT
dc.subjectFunctional near-infrared spectroscopypt_PT
dc.subjectMachine learningpt_PT
dc.subjectBrain-computer interfacept_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Exatas e da Engenhariapt_PT
dc.titleAutomatic cognitive fatigue detection using wearable fNIRS and machine learningpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleBiosignals Training Methods based on Biosignals Monitoring
oaire.awardTitleEmotional Regulation Assessment via multi-biosignal processing in a VR environment for neurorehabilitation.
oaire.awardTitleNOVA Laboratory for Computer Science and Informatics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/OE/PD%2FBDE%2F150304%2F2019/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT//2020.06024.BD/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FCEC%2F04516%2F2019/PT
oaire.citation.issue11pt_PT
oaire.citation.startPage4010pt_PT
oaire.citation.titleSensorspt_PT
oaire.citation.volume22pt_PT
oaire.fundingStreamOE
oaire.fundingStream6817 - DCRRNI ID
person.familyNameVarandas
person.familyNameLima
person.familyNameBermúdez i Badia
person.familyNamePlácido da Silva
person.familyNameGamboa
person.givenNameRui
person.givenNameRodrigo
person.givenNameSergi
person.givenNameHugo
person.givenNameHugo
person.identifier1891321
person.identifier2239636
person.identifier239789
person.identifier.ciencia-id431C-7851-485E
person.identifier.ciencia-idCA17-5E88-2B37
person.identifier.ciencia-idB415-0557-402B
person.identifier.ciencia-id841F-7D22-F80E
person.identifier.orcid0000-0002-0237-3412
person.identifier.orcid0000-0002-4030-9526
person.identifier.orcid0000-0003-4452-0414
person.identifier.orcid0000-0001-6764-8432
person.identifier.orcid0000-0002-4022-7424
person.identifier.ridC-8681-2018
person.identifier.ridM-8799-2013
person.identifier.scopus-author-id6506360007
person.identifier.scopus-author-id57200265948
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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