scholarly journals Assistive Robot Arm Controlled by a P300-based Brain Machine Interface for Daily Activities

2019 ◽  
Author(s):  
David Achanccaray ◽  
Juan M. Chau ◽  
Jairo Pirca ◽  
Francisco Sepulveda ◽  
Mitsuhiro Hayashibe
Keyword(s):  
Daily Activities ◽  
Brain Machine Interface ◽  
Robot Arm ◽  
Assistive Robot ◽  
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.

SVM-based Brain–Machine Interface for controlling a robot arm through four mental tasks

2015 ◽  
Vol 151 ◽  
pp. 116-121 ◽  
Author(s):  
E. Hortal ◽  
D. Planelles ◽  
A. Costa ◽  
E. Iáñez ◽  
A. Úbeda ◽  
...  
Keyword(s):  
Brain Machine Interface ◽  
Robot Arm ◽  
Machine Interface ◽  
Mental Tasks
Sign in / Sign up

Export Citation Format

Share Document