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dc.creatorSannelli, Claudiaes_ES
dc.creatorVidaurre, Carmenes_ES
dc.creatorMüller, Klaus Robertes_ES
dc.creatorBlankertz, Benjamines_ES
dc.date.accessioned2019-07-31T12:26:57Z
dc.date.available2019-07-31T12:26:57Z
dc.date.issued2019
dc.identifier.issn1932-6203
dc.identifier.urihttps://hdl.handle.net/2454/33792
dc.description.abstractBrain-Computer Interfaces (BCIs) are inefficient for a non-negligible part of the population, estimated around 25%. To understand this phenomenon in Sensorimotor Rhythm (SMR) based BCIs, data from a large-scale screening study conducted on 80 novice participants with the Berlin BCI system and its standard machine-learning approach were investigated. Each participant performed one BCI session with resting state Encephalography, Motor Observation, Motor Execution and Motor Imagery recordings and 128 electrodes. A significant portion of the participants (40%) could not achieve BCI control (feedback performance > 70%). Based on the performance of the calibration and feedback runs, BCI users were stratified in three groups. Analyses directed to detect and elucidate the differences in the SMR activity of these groups were performed. Statistics on reactive frequencies, task prevalence and classification results are reported. Based on their SMR activity, also a systematic list of potential reasons leading to performance drops and thus hints for possible improvements of BCI experimental design are given. The categorization of BCI users has several advantages, allowing researchers 1) to select subjects for further analyses as well as for testing new BCI paradigms or algorithms, 2) to adopt a better subject-dependent training strategy and 3) easier comparisons between different studies.en
dc.description.sponsorshipThis work was supported by German Ministry for Education and Research (BMBF) under Grants 01IS14013A-E, 01GQ1115 and 01GQ0850; Deutsche Forschungsgesellschaft (DFG) under Grant MU 987/19-1, MU987/14-1 and DFG MU 987/3-2; Brain Korea 21 Plus Program and by the Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (No. 2017-0-00451); and Spanish Ministry of Economy RYC-2014-15671.en
dc.format.extent37 p.
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherPlos oneen
dc.relation.ispartofPlos One, 2019, 14 (1)en
dc.rights© 2019 Sannelli et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectSMRbased BCIen
dc.titleA large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activityen
dc.typeinfo:eu-repo/semantics/articleen
dc.typeArtículo / Artikuluaes
dc.contributor.departmentUniversidad Pública de Navarra. Departamento de Matemáticases_ES
dc.contributor.departmentNafarroako Unibertsitate Publikoa. Matematika Sailaeu
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.identifier.doi10.1371/journal.pone.0207351
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/1PE/RYC-2014-15671en
dc.relation.publisherversionhttps://doi.org/10.1371/journal.pone.0207351
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dc.type.versionVersión publicada / Argitaratu den bertsioaes


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© 2019 Sannelli et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Except where otherwise noted, this item's license is described as © 2019 Sannelli et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.