Linked Life Data

18022247

Source:http://linkedlifedata.com/resource/pubmed/id/18022247

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2007-12-6
pubmed:abstractText
Electrocorticographic (ECoG) signals have been shown to contain reliable information about the direction of arm movements and can be used for on-line cursor control. These findings indicate that the ECoG is a potential basis for a brain-machine interface (BMI) for application in paralyzed patients. However, previous approaches to ECoG-BMIs were either based on classification of different movement patterns or on a voluntary modulation of spectral features. For a continuous multi-dimensional BMI control, the prediction of complete movement trajectories, as it has already been shown for spike data and local field potentials (LFPs), would be a desirable addition for the ECoG, too. Here, we examined ECoG signals from six subjects with subdurally implanted ECoG-electrodes during continuous two-dimensional arm movements between random target positions. Our results show that continuous trajectories of 2D hand position can be approximately predicted from the ECoG recorded from hand/arm motor cortex. This indicates that ECoG signals, related to body movements, can directly be transferred to equivalent controls of an external effector for continuous BMI control.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
0165-0270
pubmed:author
pubmed:issnType
Print
pubmed:day
15
pubmed:volume
167
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
105-14
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Prediction of arm movement trajectories from ECoG-recordings in humans.
pubmed:affiliation
Bernstein Center for Computational Neuroscience, Albert-Ludwigs-University, Freiburg, Germany. tobias.pistohl@biologie.uni-freiburg.de
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't