EEG-based endogenous online co-adaptive brain-computer interfaces: strategy for success?
| dc.contributor.author | Scherer, Reinhold | |
| dc.contributor.author | Faller, Josef | |
| dc.contributor.author | Sajda, Paul | |
| dc.contributor.author | Vidaurre Arbizu, Carmen | |
| dc.contributor.department | Estadística, Informática y Matemáticas | es_ES |
| dc.contributor.department | Estatistika, Informatika eta Matematika | eu |
| dc.date.accessioned | 2020-04-17T10:27:16Z | |
| dc.date.available | 2020-04-17T10:27:16Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | A Brain-Computer Interface (BCI) translates patterns of brain signals such as the electroencephalogram (EEG) into messages for communication and control. In the case of endogenous systems the reliable detection of induced patterns is more challenging than the detection of the more stable and stereotypical evoked responses. In the former case specific mental activities such as motor imagery are used to encode different messages. In the latter case users have to attend sensory stimuli to evoke a characteristic response. Indeed, a large number of users who try to control endogenous BCIs do not reach sufficient level of accuracy. This fact is also known as BCI “inefficiency” or “illiteracy”. In this paper we discuss and make some conjectures, based on our knowledge and experience in BCI, on whether or not online co-adaptation of human and machine can be the solution to overcome this challenge. We point out some ingredients that might be necessary for the system to be reliable and allow the users to attain sufficient control. | en |
| dc.description.sponsorship | C. Vidaurre was supported by grant number RyC-2014-15671 of the Spanish MINECO. | en |
| dc.format.extent | 6 p. | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | R. Scherer, J. Faller, P. Sajda and C. Vidaurre, 'EEG-based Endogenous Online Co-Adaptive Brain-Computer Interfaces: Strategy for Success?,' 2018 10th Computer Science and Electronic Engineering (CEEC), Colchester, United Kingdom, 2018, pp. 299-304. | en |
| dc.identifier.doi | CEEC.2018.8674198 | |
| dc.identifier.isbn | 978-153867275-4 | |
| dc.identifier.issn | 2472-1530 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/36750 | |
| dc.language.iso | eng | en |
| dc.publisher | IEEE | en |
| dc.relation.ispartof | 2018 10th Computer Science and Electronic Engineering (CEEC), Colchester, United Kingdom, 2018, pp. 299-304. | en |
| dc.relation.publisherversion | https://doi.org/10.1109/CEEC.2018.8674198 | |
| dc.rights | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work. | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.subject | Brain-computer interface (BCI) | en |
| dc.subject | Online co-adaptation | en |
| dc.subject | Electroencephalogram (EEG) | en |
| dc.title | EEG-based endogenous online co-adaptive brain-computer interfaces: strategy for success? | en |
| dc.type | info:eu-repo/semantics/conferenceObject | |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | |
| dspace.entity.type | Publication | |
| relation.isAuthorOfPublication | bfc272aa-95a8-45b2-ada9-5e679009a082 | |
| relation.isAuthorOfPublication.latestForDiscovery | bfc272aa-95a8-45b2-ada9-5e679009a082 |