Movement Type Prediction before Its Onset Using Signals from PrefrontalArea: An Electrocorticography Study

Collection with item attached
2014
Item details URL
http://open-repository.kisti.re.kr/cube/handle/open_repository/482035.do
DOI
10.1155/2014/783203
Title
Movement Type Prediction before Its Onset Using Signals from PrefrontalArea: An Electrocorticography Study
Description
This work was supported by the Global Frontier R&D Program onHuman-Centered Interaction for Coexistence funded by the NationalResearch Foundation of Korea Grant funded by the Korean Government(MSIP) (2012M3A6A3055889).
abstract
Power changes in specific frequency bands are typical brain responses during motor planning or preparation. Many studies have demonstrated that, in addition to the premotor, supplementary motor, and primary sensorimotor areas, the prefrontal area contributes to generating such responses. However, most brain-computer interface (BCI) studies have focused on the primary sensorimotor area and have estimated movements using postonset period brain signals. Our aim was to determine whether the prefrontal area could contribute to the prediction of voluntary movement types before movement onset. In our study, electrocorticography (ECoG) was recorded from six epilepsy patients while performing two self-paced tasks: hand grasping and elbow flexion. The prefrontal area was sufficient to allow classification of different movements through the area's premovement signals (-2.0 s to 0 s) in four subjects. The most pronounced power difference frequency band was the beta band (13-30Hz). The movement prediction rate during single trial estimation averaged 74% across the six subjects. Our results suggest that premovement signals in the prefrontal area are useful in distinguishing different movement tasks and that the beta band is the most informative for prediction of movement type before movement onset.
provenance
Made available in Cube on 2018-09-28T14:08:57Z (GMT). No. of bitstreams: 0
language
English
author
Ryun, Seokyun
Kim, June Sic
Lee, Sang Hun
Jeong, Sehyoon
Kim, Sung-Phil
Chung, Chun Kee
orcid
Chung, Chun Kee/0000-0003-3485-2327
accessioned
2018-09-28T14:08:57Z
available
2018-09-28T14:08:57Z
issued
2014
citation
BIOMED RESEARCH INTERNATIONAL
issn
2314-6133
uri
http://open-repository.kisti.re.kr/cube/handle/open_repository/482035.do
Funder
교육부
Funding Program
BK21플러스사업(0.5)
Project ID
1345229327
Jurisdiction
Rep.of Korea
Project Name
Strategic program of interdisciplinary human & systems engineering for technologically driven human-centered factories of the future.
rights
openAccess
type
article


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