State and trajectory decoding of upper extremity movements from electrocorticogram

Po T. Wang, Eric J. Puttock, Christine E. King, Andrew Schombs, Jack J. Lin, Mona Sazgar, Frank P K Hsu, Susan J. Shaw, David E. Millett, Charles Y. Liu, Luis A. Chui, An H. Do, Zoran Nenadic

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

Abstract

Electrocorticography has been widely explored as a long-term signal acquisition platform for brain-computer interface (BCI) control of upper extremity prostheses. However, a comprehensive study of elementary upper extremity movements and their relationship to electrocorticogram (ECoG) signals has yet to be performed. This study examines whether kinematic parameters of 6 elementary upper extremity movements can be decoded from ECoG signals in 3 subjects undergoing subdural electrode placement for epilepsy surgery evaluation. To this end, we propose a 2-stage decoding approach that consists of a state decoder to determine idle/move states, followed by a Kalman filter-based trajectory decoder. This proposed decoder successfully classified idle/move states with an average accuracy of 91%, and the correlation between decoded and measured trajectory averaged 0.70 for position and 0.68 for velocity. These performances represent an improvement over a simple regression-based approach.

Original languageEnglish (US)
Title of host publicationInternational IEEE/EMBS Conference on Neural Engineering, NER
Pages969-972
Number of pages4
DOIs
StatePublished - 2013
Externally publishedYes
Event2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013 - San Diego, CA, United States
Duration: Nov 6 2013Nov 8 2013

Other

Other2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
CountryUnited States
CitySan Diego, CA
Period11/6/1311/8/13

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

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