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  2. School of Engineering
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  4. Publikationen School of Engineering
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dc.contributor.authorJao, Ping-Keng-
dc.contributor.authorChavarriaga, Ricardo-
dc.contributor.authorDell'Agnola, Fabio-
dc.contributor.authorArza, Adriana-
dc.contributor.authorAtienza, David-
dc.contributor.authorMillan, Jose del R.-
dc.date.accessioned2021-04-15T14:00:15Z-
dc.date.available2021-04-15T14:00:15Z-
dc.date.issued2021-
dc.identifier.issn2168-2291de_CH
dc.identifier.issn2168-2305de_CH
dc.identifier.urihttps://infoscience.epfl.ch/record/282203/de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/22266-
dc.description.abstractDecoding the subjective perception of task difficulty may help improve operator performance, i.e., automatically optimize the task difficulty level. Here, we aim to decode a compound of cognitive states that covaries with the task difficulty level. We designed a protocol composed of two different subtasks, flying and visual recognition, to induce different difficulty levels. We first showed that electroencephalography (EEG) signals can be a reliable source for discriminating different compound states. To gain insight into the underlying components in the compound states, we examined the attentional index and engagement index as in our previous study. We showed that, first, attention and engagement are essential components but fail to provide the best accuracy, and, second, our model is consistent with our previous study, which means that lateralized modulations in the α bands are representative of the flying task. We also analyzed a practical issue in the design of adaptive human–machine interaction (HMI) systems, namely, the latency of changes in the user's compound state. We hypothesized that the EEG correlates of the task difficulty level do not instantaneously reflect the changes in the task difficulty. We validated the hypothesis by measuring the time required for our decoders to provide stable accuracy after the task changed. This amount of time, or latency, could be as high as ten seconds. The results suggest that the latency of changes in the user's compound state between different tasks is a factor that should be taken into account when building adaptive HMI systems.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.relation.ispartofIEEE Transactions on Human-Machine Systemsde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleEEG correlates of difficulty levels in dynamical transitions of simulated flying and mapping tasksde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.1109/THMS.2020.3038339de_CH
zhaw.funding.euNode_CH
zhaw.issue2de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end108de_CH
zhaw.pages.start99de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume51de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.funding.snf160592de_CH
zhaw.webfeedMachine Perception and Cognitionde_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedInformation Engineeringde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Jao, P.-K., Chavarriaga, R., Dell’Agnola, F., Arza, A., Atienza, D., & Millan, J. d. R. (2021). EEG correlates of difficulty levels in dynamical transitions of simulated flying and mapping tasks. IEEE Transactions on Human-Machine Systems, 51(2), 99–108. https://doi.org/10.1109/THMS.2020.3038339
Jao, P.-K. et al. (2021) ‘EEG correlates of difficulty levels in dynamical transitions of simulated flying and mapping tasks’, IEEE Transactions on Human-Machine Systems, 51(2), pp. 99–108. Available at: https://doi.org/10.1109/THMS.2020.3038339.
P.-K. Jao, R. Chavarriaga, F. Dell’Agnola, A. Arza, D. Atienza, and J. d. R. Millan, “EEG correlates of difficulty levels in dynamical transitions of simulated flying and mapping tasks,” IEEE Transactions on Human-Machine Systems, vol. 51, no. 2, pp. 99–108, 2021, doi: 10.1109/THMS.2020.3038339.
JAO, Ping-Keng, Ricardo CHAVARRIAGA, Fabio DELL’AGNOLA, Adriana ARZA, David ATIENZA und Jose del R. MILLAN, 2021. EEG correlates of difficulty levels in dynamical transitions of simulated flying and mapping tasks. IEEE Transactions on Human-Machine Systems [online]. 2021. Bd. 51, Nr. 2, S. 99–108. DOI 10.1109/THMS.2020.3038339. Verfügbar unter: https://infoscience.epfl.ch/record/282203/
Jao, Ping-Keng, Ricardo Chavarriaga, Fabio Dell’Agnola, Adriana Arza, David Atienza, and Jose del R. Millan. 2021. “EEG Correlates of Difficulty Levels in Dynamical Transitions of Simulated Flying and Mapping Tasks.” IEEE Transactions on Human-Machine Systems 51 (2): 99–108. https://doi.org/10.1109/THMS.2020.3038339.
Jao, Ping-Keng, et al. “EEG Correlates of Difficulty Levels in Dynamical Transitions of Simulated Flying and Mapping Tasks.” IEEE Transactions on Human-Machine Systems, vol. 51, no. 2, 2021, pp. 99–108, https://doi.org/10.1109/THMS.2020.3038339.


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