Decoding the evolving grasping gesture from electroencephalographic (EEG) activity

Harshavardhan A. Agashe, Jose L. Contreras-Vidal

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

13 Scopus citations

Abstract

Shared control is emerging as a likely strategy for controlling neuroprosthetic devices, in which users specify high level goals but the low-level implementation is carried out by the machine. In this context, predicting the discrete goal is necessary. Although grasping various objects is critical in determining independence in daily life of amputees, decoding of different grasp types from noninvasively recorded brain activity has not been investigated. Here we show results suggesting electroencephalography (EEG) is a feasible modality to extract information on grasp types from the user's brain activity. We found that the information about the intended grasp increases over the grasping movement, and is significantly greater than chance up to 200 ms before movement onset.

Original languageEnglish
Title of host publicationProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Pages5590-5593
Number of pages4
Volume2013
DOIs
StatePublished - Oct 31 2013
Event2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 - Osaka, Japan
Duration: Jul 3 2013Jul 7 2013

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Other

Other2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
CountryJapan
CityOsaka
Period7/3/137/7/13

ASJC Scopus subject areas

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

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