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Optimizing performance of non-expert users in brain-computer interaction by means of an adaptive performance engine

2015Conference object Open access
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dc.contributor.authorFerreira, André
dc.contributor.authorVourvopoulos, Athanasios
dc.contributor.authorBermúdez i Badia, Sergi
dc.date.accessioned2019-12-06T09:40:19Z
dc.date.available2019-12-06T09:40:19Z
dc.date.issued2015
dc.description.abstractBrain–Computer Interfaces (BCIs) are become increasingly more available at reduced costs and are being incorporated into immersive virtual environments and video games for serious applications. Most research in BCIs focused on signal processing techniques and has neglected the interaction aspect of BCIs. This has created an imbalance between BCI classification performance and online control quality of the BCI interaction. This results in user fatigue and loss of interest over time. In the health domain, BCIs provide a new way to overcome motor-related disabilities, promoting functional and structural plasticity in the brain. In order to exploit the advantages of BCIs in neurorehabilitation we need to maximize not only the classification performance of such systems but also engagement and the sense of competence of the user. Therefore, we argue that the primary goal should not be for users to be trained to successfully use a BCI system but to adapt the BCI interaction to each user in order to maximize the level of control on their actions, whatever their performance level is. To achieve this, we developed the Adaptive Performance Engine (APE) and tested with data from 20 naïve BCI users. APE can provide user specific performance improvements up to approx. 20% and we compare it with previous methods. Finally, we contribute with an open motor-imagery datasets with 2400 trials from naïve users.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationFerreira, A., Vourvopoulos, A., & Bermúdez i Badia, S. (2015). Optimizing performance of non-expert users in brain-computer interaction by means of an adaptive performance engine. In International Conference on Brain Informatics and Health (pp. 202-211). Springer, Cham.pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/2624
dc.language.isoengpt_PT
dc.publisherSpringerpt_PT
dc.relationMULTIMODAL NEURO-ROBOTIC VR SYSTEM FOR STROKE REHABILITATION
dc.subjectBrain-computer interfacespt_PT
dc.subjectAdaptive performancept_PT
dc.subjectMotor imagerypt_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Exatas e da Engenhariapt_PT
dc.titleOptimizing performance of non-expert users in brain-computer interaction by means of an adaptive performance enginept_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleMULTIMODAL NEURO-ROBOTIC VR SYSTEM FOR STROKE REHABILITATION
oaire.awardURIinfo:eu-repo/grantAgreement/FCT//SFRH%2FBD%2F97117%2F2013/PT
oaire.citation.endPage211pt_PT
oaire.citation.startPage202pt_PT
oaire.citation.titleInternational Conference on Brain Informatics and Healthpt_PT
person.familyNameVourvopoulos
person.familyNameBermúdez i Badia
person.givenNameAthanasios
person.givenNameSergi
person.identifier279446
person.identifier239789
person.identifier.ciencia-id5813-A481-A9D3
person.identifier.ciencia-idCA17-5E88-2B37
person.identifier.orcid0000-0001-9676-8599
person.identifier.orcid0000-0003-4452-0414
person.identifier.ridF-3872-2017
person.identifier.ridC-8681-2018
person.identifier.scopus-author-id48762198300
person.identifier.scopus-author-id6506360007
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication9f85dbdf-dbe3-46a4-b625-d5cc2cdcc83c
relation.isAuthorOfPublicationef8f1e3b-3c09-4817-80d0-d96aa88051a2
relation.isAuthorOfPublication.latestForDiscovery9f85dbdf-dbe3-46a4-b625-d5cc2cdcc83c
relation.isProjectOfPublicationac4cd4c1-be3a-4e30-8c97-b553ebff6074
relation.isProjectOfPublication.latestForDiscoveryac4cd4c1-be3a-4e30-8c97-b553ebff6074

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