Bubble

Robot

Robotic arm

Algorithm

Engineering

Neuroscience

Joint (building)

Neurorehabilitation

Parallel computing

Computer science

Maximum bubble pressure method

Brain–computer interface

Neuroprosthetics

Rehabilitation

Psychology

Interface (matter)

Artificial intelligence

Architectural engineering

Electroencephalography

Medicine

Physical medicine and rehabilitation

Electrocorticography

Computer vision

Computational Intelligence and Neuroscience

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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mo>±</mml:mo></mml:math> 0.0033 (muscle activations) and 0.6359 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mo>±</mml:mo></mml:math> 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.

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