Full metadata record
DC Field | Value | Language |
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dc.contributor.author | 권규현 | - |
dc.date.accessioned | 2017-03-30T07:26:40Z | - |
dc.date.available | 2017-03-30T07:26:40Z | - |
dc.date.issued | 2015-07 | - |
dc.identifier.citation | NEUROIMAGE, v. 113, Page. 101-110 | en_US |
dc.identifier.issn | 1053-8119 | - |
dc.identifier.uri | http://www.sciencedirect.com/science/article/pii/S1053811915002153 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11754/26457 | - |
dc.description.abstract | In brain-computer interfacing (BCI), motor imagery is used to provide a gateway to an effector action or behavior. However, in contrast to the main functional role of the primary motor cortex (M1) in motor execution, the M1's involvement in motor imagery has been debated, while the roles of secondary motor areas such as the premotor cortex (PMC) and supplementary motor area (SMA) in motor imagery have been proposed. We examined which motor cortical region had the greatest predictive ability for imagined movement among the primary and secondary motor areas. For two modes of motor performance, executed movement and imagined movement, in 12 healthy subjects who performed two types of motor task, hand grasping and hand rotation, we used the multivariate Bayes method to compare predictive ability between the primary and secondary motor areas (M1, PMC, and SMA) contralateral to the moved hand. With the distributed representation of activation, executed movement was best predicted from the M1 while imagined movement from the SMA, among the three motor cortical regions, in both types of motor task. In addition, the most predictive information about the distinction between executed movement and imagined movement was contained in the M1. The greater predictive ability of the SMA for imagined movement suggests its functional role that could be applied to motor imagery-based BCI. (C) 2015 Elsevier Inc. All rights reserved. | en_US |
dc.description.sponsorship | We thank the reviewers for their constructive remarks. This study was supported by the SMC-KIST Translational Research Program in 2012 and the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIP) (NRF- 2014R1A2A1A01005128). | en_US |
dc.language.iso | en | en_US |
dc.publisher | ACADEMIC PRESS INC ELSEVIER SCIENCE | en_US |
dc.subject | fMRI | en_US |
dc.subject | Multivarlate Bayes | en_US |
dc.subject | Motor imagery | en_US |
dc.subject | Motor execution | en_US |
dc.subject | Motor cortex | en_US |
dc.title | Which motor cortical region best predicts imagined movement? | en_US |
dc.type | Article | en_US |
dc.relation.volume | 113 | - |
dc.identifier.doi | 10.1016/j.neuroimage.2015.03.033 | - |
dc.relation.page | 101-110 | - |
dc.relation.journal | NEUROIMAGE | - |
dc.contributor.googleauthor | Park, Chang-hyun | - |
dc.contributor.googleauthor | Chang, Won Hyuk | - |
dc.contributor.googleauthor | Lee, Minji | - |
dc.contributor.googleauthor | Kwon, Gyu Hyun | - |
dc.contributor.googleauthor | Kim, Laehyun | - |
dc.contributor.googleauthor | Kim, Sung Tae | - |
dc.contributor.googleauthor | Kim, Yun-Hee | - |
dc.relation.code | 2015001993 | - |
dc.sector.campus | S | - |
dc.sector.daehak | GRADUATE SCHOOL OF TECHNOLOGY & INNOVATION MANAGEMENT[S] | - |
dc.sector.department | DEPARTMENT OF TECHNOLOGY MANAGEMENT | - |
dc.identifier.pid | ghkwon | - |
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