Brain-machine interface control of a robot arm using actor-critic rainforcement learning

2012 ◽  
Author(s):  
E. A. Pohlmeyer ◽  
B. Mahmoudi ◽  
Shijia Geng ◽  
N. Prins ◽  
J. C. Sanchez
Keyword(s):  
Brain Machine Interface ◽  
Robot Arm ◽  
Interface Control ◽  
Machine Interface

scholarly journals Exploiting Task Constraints for Self-Calibrated Brain-Machine Interface Control Using Error-Related Potentials

PLoS ONE ◽  
2015 ◽  
Vol 10 (7) ◽  
pp. e0131491 ◽  
Author(s):  
Iñaki Iturrate ◽  
Jonathan Grizou ◽  
Jason Omedes ◽  
Pierre-Yves Oudeyer ◽  
Manuel Lopes ◽  
...  
Keyword(s):  
Brain Machine Interface ◽  
Task Constraints ◽  
Interface Control ◽  
Related Potentials ◽  
Machine Interface

scholarly journals Intrinsic Variable Learning for Brain-Machine Interface Control by Human Anterior Intraparietal Cortex

Neuron ◽  
2019 ◽  
Vol 102 (3) ◽  
pp. 694-705.e3 ◽  
Author(s):  
Sofia Sakellaridi ◽  
Vassilios N. Christopoulos ◽  
Tyson Aflalo ◽  
Kelsie W. Pejsa ◽  
Emily R. Rosario ◽  
...  
Keyword(s):  
Brain Machine Interface ◽  
Interface Control ◽  
Machine Interface ◽  
Intrinsic Variable

Brain-Machine Interface Control via Reinforcement Learning

2007 ◽  
Author(s):  
Jack DiGiovanna ◽  
Babak Mahmoudi ◽  
Jeremiah Mitzelfelt ◽  
Justin C. Sanchez ◽  
Jose C. Principe
Keyword(s):  
Reinforcement Learning ◽  
Brain Machine Interface ◽  
Interface Control ◽  
Machine Interface

scholarly journals Using Reinforcement Learning to Provide Stable Brain-Machine Interface Control Despite Neural Input Reorganization

PLoS ONE ◽  
2014 ◽  
Vol 9 (1) ◽  
pp. e87253 ◽  
Author(s):  
Eric A. Pohlmeyer ◽  
Babak Mahmoudi ◽  
Shijia Geng ◽  
Noeline W. Prins ◽  
Justin C. Sanchez
Keyword(s):  
Reinforcement Learning ◽  
Brain Machine Interface ◽  
Interface Control ◽  
Neural Input ◽  
Machine Interface

Development of Brain Machine Interface for Operation of Auxiliary Robot Arm

2019 ◽  
Vol 2019 (0) ◽  
pp. 2A2-L05
Author(s):  
Yoshiyuki Tsuda ◽  
Shoshiro Hatakeyama ◽  
Masami Iwase ◽  
Jun Inoue
Keyword(s):  
Brain Machine Interface ◽  
Robot Arm ◽  
Machine Interface

scholarly journals Control of a Robot Arm Using Decoded Joint Angles from Electrocorticograms in Primate

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Duk Shin ◽  
Hiroyuki Kambara ◽  
Natsue Yoshimura ◽  
Yasuharu Koike
Keyword(s):  
Linear Regression ◽  
Degree Of Freedom ◽  
Coefficient Of Determination ◽  
Brain Machine Interface ◽  
Robot Arm ◽  
Joint Angles ◽  
Machine Interface ◽  
Electrocorticogram Ecog ◽  
Muscle Activations ◽  
Best Fit

Electrocorticogram (ECoG) is a well-known recording method for the less invasive brain machine interface (BMI). Our previous studies have succeeded in predicting muscle activities and arm trajectories from ECoG signals. Despite such successful studies, there still remain solving works for the purpose of realizing an ECoG-based prosthesis. We suggest a neuromuscular interface to control robot using decoded muscle activities and joint angles. We used sparse linear regression to find the best fit between band-passed ECoGs and electromyograms (EMG) or joint angles. The best coefficient of determination for 100 s continuous prediction was 0.6333 ± 0.0033 (muscle activations) and 0.6359 ± 0.0929 (joint angles), respectively. We also controlled a 4 degree of freedom (DOF) robot arm using only decoded 4 DOF angles from the ECoGs in this study. Consequently, this study shows the possibility of contributing to future advancements in neuroprosthesis and neurorehabilitation technology.

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