Linked Life Data

20192030

Source:http://linkedlifedata.com/resource/pubmed/id/20192030

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2010-3-2
pubmed:abstractText
Recent advances in brain activity analysis and computational algorithms have enabled people with severe motor disorders to control external devices via brain activity. Brain-machine interface (BMI)/brain-computer interface has gained importance as the ultimate strategy for functional compensation because it improves impaired neuromuscular systems. Invasive BMI performed using needle arrays can best control robotic arms or computer cursors because it records neural activity in the primary motor cortex in detail. The extensive and validated physiological background of recorded signals enables researchers to develop highly accurate BMI systems with needle electrodes. Less invasive neural recording with an electrocorticogram (ECoG)-electrode array provides good temporal and spatial information for use in prosthetic control. ECoG electrodes have wide clinical applications in pain control and epilepsy; therefore, techniques for electrode implantation are well established compared to those for needle arrays. These electrodes may find wide clinical applications if their accuracy level reaches that suitable for practical use. Noninvasive BMI involving neural recording by electroencephalography (EEG) is the most widely used technique because of a convenient experimental setup, although it provides a limited range of decodable motor outputs. In EEG, arc-shaped mu rhythms of 8-12 Hz appear around the sensorimotor area in the resting state and diminish in amplitude during motor imagery. Thus, the mu rhythm amplitude may correlate with cortical excitability of the sensorimotor area, and EEG-BMI may be useful in the neurorehabilitation of patients with stroke-induced hemiplegia. Research on BMI as a therapeutic tool though emergent, may widen the scope of conventional BMI.
pubmed:language
jpn
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
1881-6096
pubmed:author
pubmed:issnType
Print
pubmed:volume
62
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
101-11
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
[Brain-machine interface--current status and future prospects].
pubmed:affiliation
Department of Biosciences and Informatics, Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa 223-8522, Japan.
pubmed:publicationType
Journal Article, English Abstract, Review