Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Spatial covariance improves BCI performance for late ERPs components with high temporal variability
Authors: Aydarkhanov, Ruslan
Ušćumlić, Marija
Chavarriaga, Ricardo
Gheorghe, Lucian
del R Millán, José
et. al: No
DOI: 10.1088/1741-2552/ab95eb
Published in: Journal of Neural Engineering
Volume(Issue): 17
Issue: 3
Pages: 036030
Issue Date: 25-Jun-2020
Publisher / Ed. Institution: Institute of Physics Publishing
ISSN: 1741-2552
Language: English
Subject (DDC): 004: Computer science
Abstract: Event 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.
URI: https://digitalcollection.zhaw.ch/handle/11475/20509
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Appears in Collections:Publikationen School of Engineering

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