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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-28455
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dc.contributor.authorIwane, Fumiaki-
dc.contributor.authorSobolewski, Aleksander-
dc.contributor.authorChavarriaga, Ricardo-
dc.contributor.authorMillán, José del R.-
dc.date.accessioned2023-08-11T14:33:36Z-
dc.date.available2023-08-11T14:33:36Z-
dc.date.issued2023-
dc.identifier.issn2589-0042de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/28455-
dc.description.abstractError-related potentials (ErrP) are a prominent electroencephalogram (EEG) correlate of performance monitoring, and so crucial for learning and adapting our behavior. It is poorly understood whether ErrPs encode further information beyond error awareness. We report an experiment with sixteen participants over three sessions in which occasional visual rotations of varying magnitude occurred during a cursor reaching task. We designed a brain-computer interface (BCI) to detect ErrP that provided real-time feedback. The individual ErrP-BCI decoders exhibited good transfer across sessions and scalability over the magnitude of errors. A non-linear relationship between the ErrP-BCI output and the magnitude of errors predicts individual perceptual thresholds to detect errors. We also reveal theta-gamma oscillatory coupling that co-varied with the magnitude of the required adjustment. Our findings open new avenues to probe and extend current theories of performance monitoring by incorporating continuous human-interaction tasks and analysis of the ErrP complex rather than individual peaks.de_CH
dc.language.isoende_CH
dc.publisherCell Pressde_CH
dc.relation.ispartofiSciencede_CH
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/de_CH
dc.subjectEEGde_CH
dc.subjectBrain-computer interfacede_CH
dc.subjectError-related potentialde_CH
dc.subjectMagnitude of errorsde_CH
dc.subjecttheta-gamma oscillatory couplingde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc150: Psychologiede_CH
dc.titleEEG error-related potentials encode magnitude of errors and individual perceptual thresholdsde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitCentre for Artificial Intelligence (CAI)de_CH
dc.identifier.doi10.1016/j.isci.2023.107524de_CH
dc.identifier.doi10.21256/zhaw-28455-
zhaw.funding.euNode_CH
zhaw.issue9de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.start107524de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume26de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedMachine Perception and Cognitionde_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedDIZH Fellowshipde_CH
zhaw.webfeedZHAW digitalde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Iwane, F., Sobolewski, A., Chavarriaga, R., & Millán, J. d. R. (2023). EEG error-related potentials encode magnitude of errors and individual perceptual thresholds. iScience, 26(9), 107524. https://doi.org/10.1016/j.isci.2023.107524
Iwane, F. et al. (2023) ‘EEG error-related potentials encode magnitude of errors and individual perceptual thresholds’, iScience, 26(9), p. 107524. Available at: https://doi.org/10.1016/j.isci.2023.107524.
F. Iwane, A. Sobolewski, R. Chavarriaga, and J. d. R. Millán, “EEG error-related potentials encode magnitude of errors and individual perceptual thresholds,” iScience, vol. 26, no. 9, p. 107524, 2023, doi: 10.1016/j.isci.2023.107524.
IWANE, Fumiaki, Aleksander SOBOLEWSKI, Ricardo CHAVARRIAGA und José del R. MILLÁN, 2023. EEG error-related potentials encode magnitude of errors and individual perceptual thresholds. iScience. 2023. Bd. 26, Nr. 9, S. 107524. DOI 10.1016/j.isci.2023.107524
Iwane, Fumiaki, Aleksander Sobolewski, Ricardo Chavarriaga, and José del R. Millán. 2023. “EEG Error-Related Potentials Encode Magnitude of Errors and Individual Perceptual Thresholds.” iScience 26 (9): 107524. https://doi.org/10.1016/j.isci.2023.107524.
Iwane, Fumiaki, et al. “EEG Error-Related Potentials Encode Magnitude of Errors and Individual Perceptual Thresholds.” iScience, vol. 26, no. 9, 2023, p. 107524, https://doi.org/10.1016/j.isci.2023.107524.


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