Electrocorticogram encoding of upper extremity movement trajectories

Po T. Wang, 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, Zoran Nenadic, An H. Do

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

6 Scopus citations

Abstract

Electrocorticogram (ECoG)-based brain computer interfaces (BCI) can potentially control upper extremity pros-theses to restore independent function to paralyzed individuals. However, current research is mostly restricted to the offline decoding of finger or 2D arm movement trajectories, and these results are modest. This study seeks to improve the fundamental understanding of the ECoG signal features underlying upper extremity movements to guide better BCI design. Subjects undergoing ECoG electrode implantation performed a series of elementary upper extremity movements in an intermittent flexion and extension manner. It was found that movement velocity, θ̇, had a high positive (negative) correlation with the instantaneous power of the ECoG high-γ band (80-160 Hz) during flexion (extension). Also, the correlation was low during idling epochs. Visual inspection of the ECoG high-γ band revealed power bursts during flexion/extension events that had a waveform that strongly resembled the corresponding flexion/extension event as seen on θ̇. These high-γ bursts were present in all elementary movements, and were spatially distributed in a somatotopic fashion. Thus, it can be concluded that the high-γ power of ECoG strongly encodes for movement trajectories, and can be used as an input feature in future BCIs.

Original languageEnglish (US)
Title of host publication2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
Pages1429-1432
Number of pages4
DOIs
StatePublished - 2013
Event2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013 - San Diego, CA, United States
Duration: Nov 6 2013Nov 8 2013

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Other

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

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

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