Rediction of joint angle from muscle activities decoded from electrocorticograms

Duk Shin, Chao Chen, Yasuhiko Nakanishi, Hiroyuki Kambara, Natsue Yoshimura, Hidenori Watanabe, Atsushi Nambu, Tadashi Isa, Yukio Nishimura, Yasuharu Koike

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

Abstract

Electrocorticogram (ECoG) has drawn attention as an effective recording approach for less invasive brain-machine interfaces (BMI). Previous studies succeeded in classifying the movement direction or velocity from ECoGs. Despite such successful studies, there still remain considerable works for the purpose of realizing an ECoG-based BMI robot. Our previous study suggested and verified the method to predict multiple muscle activities from ECoG measurements. In this article, we predicted 4 DOF angle of arm from muscle activities decoded from ECoG signals. We also controlled 4 DOF robot arm using the predicted angle. Consequently, this study shows that it could derive online prediction of angle of arm from ECoG signals.

Original languageEnglish
Title of host publication5th International Symposium on Measurement, Analysis and Modelling of Human Functions, ISHF 2013
PublisherIMEKO-International Measurement Federation Secretariat
Pages65-66
Number of pages2
ISBN (Print)9781632660251
Publication statusPublished - 2013
Externally publishedYes
Event5th International Symposium on Measurement, Analysis and Modelling of Human Functions, ISHF 2013 - Vancouver, BC, Canada
Duration: 2013 Jun 272013 Jun 29

Publication series

Name5th International Symposium on Measurement, Analysis and Modelling of Human Functions, ISHF 2013

Other

Other5th International Symposium on Measurement, Analysis and Modelling of Human Functions, ISHF 2013
CountryCanada
CityVancouver, BC
Period13/6/2713/6/29

Keywords

  • Brain machine interface
  • EMG
  • Electrocorticogram

ASJC Scopus subject areas

  • Modelling and Simulation

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