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dc.creatorHiremath, SV
dc.creatorChen, W
dc.creatorWang, W
dc.creatorFoldes, S
dc.creatorYang, Y
dc.creatorTyler-Kabara, EC
dc.creatorCollinger, JL
dc.creatorBoninger, ML
dc.date.accessioned2021-01-29T21:40:09Z
dc.date.available2021-01-29T21:40:09Z
dc.date.issued2015-06-10
dc.identifier.issn1662-5145
dc.identifier.issn1662-5145
dc.identifier.doihttp://dx.doi.org/10.34944/dspace/5205
dc.identifier.otherPMC4462099 (pmc)
dc.identifier.urihttp://hdl.handle.net/20.500.12613/5223
dc.description.abstract© 2015 Hiremath, Chen, Wang, Foldes, Yang, Tyler-Kabara, Collinger and Boninger. A brain-computer interface (BCI) system transforms neural activity into control signals for external devices in real time. A BCI user needs to learn to generate specific cortical activity patterns to control external devices effectively. We call this process BCI learning, and it often requires significant effort and time. Therefore, it is important to study this process and develop novel and efficient approaches to accelerate BCI learning. This article reviews major approaches that have been used for BCI learning, including computer-assisted learning, co-adaptive learning, operant conditioning, and sensory feedback. We focus on BCIs based on electrocorticography and intracortical microelectrode arrays for restoring motor function. This article also explores the possibility of brain modulation techniques in promoting BCI learning, such as electrical cortical stimulation, transcranial magnetic stimulation, and optogenetics. Furthermore, as proposed by recent BCI studies, we suggest that BCI learning is in many ways analogous to motor and cognitive skill learning, and therefore skill learning should be a useful metaphor to model BCI learning.
dc.format.extent1-10
dc.language.isoeng
dc.relation.haspartFrontiers in Integrative Neuroscience
dc.relation.isreferencedbyFrontiers Media SA
dc.rightsCC BY
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectBCI learning
dc.subjectBCI mapping
dc.subjectbrain control
dc.subjectcognitive skill learning
dc.subjecthuman-computer interfaces
dc.subjectmotor learning
dc.titleBrain computer interface learning for systems based on electrocorticography and intracortical microelectrode arrays
dc.typeArticle
dc.type.genreJournal Article
dc.relation.doi10.3389/fnint.2015.00040
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.date.updated2021-01-29T21:40:06Z
refterms.dateFOA2021-01-29T21:40:10Z


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