Decoding hand and cursor kinematics from magnetoencephalographic signals during tool use

Trent J. Bradberry, José L. Contreras-Vidal, Feng Rong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

The ability to decode kinematics of intended movement from neural activity is essential for the development of prosthetic devices, such as artificial arms, that can aid motor-disabled persons. To date, most of the progress in the development of neuromotor prostheses has been obtained by decoding neural activity acquired through invasive means, such as microelectrode arrays seated into motor cortical tissue. In this study, we demonstrate the feasibility of decoding both hand position and velocity from non-invasive magnetoencephalographic signals during a center-out drawing task in familiar and novel environments. The mean correlation coefficients between measured and decoded kinematics ranged from 0.27-0.61 for the horizontal dimension of movement and 0.06-0.58 for the vertical dimension. Our results indicate that non-invasive neuroimaging signals may contain sufficient kinematic information for controlling neuromotor prostheses.

Original languageEnglish (US)
Title of host publicationProceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5306-5309
Number of pages4
ISBN (Print)9781424418152
DOIs
StatePublished - 2008
Event30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - Vancouver, BC, Canada
Duration: Aug 20 2008Aug 25 2008

Publication series

NameProceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology"

Other

Other30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08
CountryCanada
CityVancouver, BC
Period8/20/088/25/08

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

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