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  2. School of Engineering
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  4. Publikationen School of Engineering
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dc.contributor.authorAydarkhanov, Ruslan-
dc.contributor.authorUšćumlić, Marija-
dc.contributor.authorChavarriaga, Ricardo-
dc.contributor.authorGheorghe, Lucian-
dc.contributor.authordel R Millán, José-
dc.date.accessioned2020-09-17T13:09:37Z-
dc.date.available2020-09-17T13:09:37Z-
dc.date.issued2020-06-25-
dc.identifier.issn1741-2552de_CH
dc.identifier.urihttps://infoscience.epfl.ch/record/280708/de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/20509-
dc.description.abstractEvent Related Potentials (ERPs) reflecting cognitive response to external stimuli, are widely used in brain computer interfaces. ERP waveforms are characterized by a series of components of particular latency and amplitude. The classical ERP decoding methods exploit this waveform characteristic and thus achieve a high performance only if there is sufficient time- and phase-locking across trials. The required condition is not fulfilled if the experimental tasks are challenging or if it is needed to generalize across various experimental conditions. Features based on spatial covariances across channels can potentially overcome the latency jitter and delays since they aggregate the information across time.de_CH
dc.language.isoende_CH
dc.publisherIOP Publishingde_CH
dc.relation.ispartofJournal of Neural Engineeringde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subject.ddc004: Informatikde_CH
dc.titleSpatial covariance improves BCI performance for late ERPs components with high temporal variabilityde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.1088/1741-2552/ab95ebde_CH
dc.identifier.pmid32442981de_CH
zhaw.funding.euNode_CH
zhaw.issue3de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.start036030de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume17de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedMachine Perception and Cognitionde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Aydarkhanov, R., Ušćumlić, M., Chavarriaga, R., Gheorghe, L., & del R Millán, J. (2020). Spatial covariance improves BCI performance for late ERPs components with high temporal variability. Journal of Neural Engineering, 17(3), 36030. https://doi.org/10.1088/1741-2552/ab95eb
Aydarkhanov, R. et al. (2020) ‘Spatial covariance improves BCI performance for late ERPs components with high temporal variability’, Journal of Neural Engineering, 17(3), p. 036030. Available at: https://doi.org/10.1088/1741-2552/ab95eb.
R. Aydarkhanov, M. Ušćumlić, R. Chavarriaga, L. Gheorghe, and J. del R Millán, “Spatial covariance improves BCI performance for late ERPs components with high temporal variability,” Journal of Neural Engineering, vol. 17, no. 3, p. 036030, Jun. 2020, doi: 10.1088/1741-2552/ab95eb.
AYDARKHANOV, Ruslan, Marija UŠĆUMLIĆ, Ricardo CHAVARRIAGA, Lucian GHEORGHE und José DEL R MILLÁN, 2020. Spatial covariance improves BCI performance for late ERPs components with high temporal variability. Journal of Neural Engineering [online]. 25 Juni 2020. Bd. 17, Nr. 3, S. 036030. DOI 10.1088/1741-2552/ab95eb. Verfügbar unter: https://infoscience.epfl.ch/record/280708/
Aydarkhanov, Ruslan, Marija Ušćumlić, Ricardo Chavarriaga, Lucian Gheorghe, and José del R Millán. 2020. “Spatial Covariance Improves BCI Performance for Late ERPs Components with High Temporal Variability.” Journal of Neural Engineering 17 (3): 36030. https://doi.org/10.1088/1741-2552/ab95eb.
Aydarkhanov, Ruslan, et al. “Spatial Covariance Improves BCI Performance for Late ERPs Components with High Temporal Variability.” Journal of Neural Engineering, vol. 17, no. 3, June 2020, p. 36030, https://doi.org/10.1088/1741-2552/ab95eb.


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