Linked Life Data

17694874

Source:http://linkedlifedata.com/resource/pubmed/id/17694874

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
8
pubmed:dateCreated
2007-8-14
pubmed:abstractText
Current demonstrations of brain-machine interfaces (BMIs) have shown the potential for controlling neuroprostheses under pure motion control. For interaction with objects, however, pure motion control lacks the information required for versatile manipulation. This paper investigates the idea of applying impedance control in a BMI system. An extraction algorithm incorporating a musculoskeletal arm model was developed for this purpose. The new algorithm, called the muscle activation method (MAM), was tested on cortical recordings from a behaving monkey. The MAM was found to predict motion parameters with as much accuracy as a linear filter. Furthermore, it successfully predicted limb interactions with novel force fields, which is a new and significant capability lacking in other algorithms.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
0018-9294
pubmed:author
pubmed:issnType
Print
pubmed:volume
54
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1520-9
pubmed:dateRevised
2009-11-11
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
The muscle activation method: an approach to impedance control of brain-machine interfaces through a musculoskeletal model of the arm.
pubmed:affiliation
Mechatronics and Manufacturing Technology Center, Samsung Electronics, Suwon, Korea. hyunkim@mit.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Research Support, Non-U.S. Gov't