Long-term decoding of arm movement using Spatial Distribution of Neural Patterns

Vijay Aditya Tadipatri, Ahmed H. Tewfik, James Ashe

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Day to day variability and non-stationarity caused by changes in subject motivation, learning and behavior pose a challenge in using local field potentials (LFP) for practical Brain Computer Interfaces. Pattern recognition algorithms require that the features possess little to no variation from the training to test data. As such models developed on one day fail to represent the characteristics on the other day. This paper provides a solution in the form of adaptive spatial features. We propose an algorithm to capture the local spatial variability of LFP patterns and provide accurate long-term decoding. This algorithm achieved more than 95% decoding of eight movement directions two weeks after its initial training.

Original languageEnglish (US)
Title of host publication2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1642-1645
Number of pages4
Volume2014
ISBN (Electronic)9781424479290
DOIs
StatePublished - Jan 1 2014
Event2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 - Chicago, United States
Duration: Aug 26 2014Aug 30 2014

Publication series

NameConference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISSN (Print)1557-170X

Other

Other2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
CountryUnited States
CityChicago
Period8/26/148/30/14

Keywords

  • Brain Computer Interface
  • Local Field Potentials
  • Long-term decoding

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