REDICTION OF JOINT ANGLE FROM MUSCLE ACTIVITIES DECODED FROM ELECTROCORTICOGRAMS |
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| Duk Shin, Chao Chen, Yasuhiko Nakanishi, Hiroyuki Kambara, Natsue Yoshimura, Hidenori Watanabe, Atsushi Nambu, Tadashi Isa, Yukio Nishimura, Yasuharu Koike |
- 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.
- Keywords:
- brain machine interface, electrocorticogram, EMG
- Download:
- IMEKO-TC18-2013-013.pdf.pdf
- DOI:
- -
- Event details
- IMEKO TC:
- TC18
- Event name:
- TC18 Symposium 2013
- Title:
- 5th Symposium on Measurement, Analysis and Modeling of Human Functions
- Place:
- Vancouver, CANADA
- Time:
- 27 June 2013 - 29 June 2013