Operant conditioning of a multiple degree-of-freedom brain-machine interface in a primate model of amputation

Author(s):  
Karthikeyan Balasubramanian ◽  
Joshua Southerland ◽  
Mukta Vaidya ◽  
Kai Qian ◽  
Ahmed Eleryan ◽  
...  
2018 ◽  
Vol 119 (4) ◽  
pp. 1291-1304 ◽  
Author(s):  
Mukta Vaidya ◽  
Karthikeyan Balasubramanian ◽  
Joshua Southerland ◽  
Islam Badreldin ◽  
Ahmed Eleryan ◽  
...  

The development of coordinated reach-to-grasp movement has been well studied in infants and children. However, the role of motor cortex during this development is unclear because it is difficult to study in humans. We took the approach of using a brain-machine interface (BMI) paradigm in rhesus macaques with prior therapeutic amputations to examine the emergence of novel, coordinated reach to grasp. Previous research has shown that after amputation, the cortical area previously involved in the control of the lost limb undergoes reorganization, but prior BMI work has largely relied on finding neurons that already encode specific movement-related information. In this study, we taught macaques to cortically control a robotic arm and hand through operant conditioning, using neurons that were not explicitly reach or grasp related. Over the course of training, stereotypical patterns emerged and stabilized in the cross-covariance between the reaching and grasping velocity profiles, between pairs of neurons involved in controlling reach and grasp, and to a comparable, but lesser, extent between other stable neurons in the network. In fact, we found evidence of this structured coordination between pairs composed of all combinations of neurons decoding reach or grasp and other stable neurons in the network. The degree of and participation in coordination was highly correlated across all pair types. Our approach provides a unique model for studying the development of novel, coordinated reach-to-grasp movement at the behavioral and cortical levels. NEW & NOTEWORTHY Given that motor cortex undergoes reorganization after amputation, our work focuses on training nonhuman primates with chronic amputations to use neurons that are not reach or grasp related to control a robotic arm to reach to grasp through the use of operant conditioning, mimicking early development. We studied the development of a novel, coordinated behavior at the behavioral and cortical level, and the neural plasticity in M1 associated with learning to use a brain-machine interface.


2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Duk Shin ◽  
Hiroyuki Kambara ◽  
Natsue Yoshimura ◽  
Yasuharu Koike

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.


2013 ◽  
Vol 33 (19) ◽  
pp. 8308-8320 ◽  
Author(s):  
P.-J. Arduin ◽  
Y. Fregnac ◽  
D. E. Shulz ◽  
V. Ego-Stengel

Neuron ◽  
2013 ◽  
Vol 77 (2) ◽  
pp. 361-375 ◽  
Author(s):  
Ben Engelhard ◽  
Nofar Ozeri ◽  
Zvi Israel ◽  
Hagai Bergman ◽  
Eilon Vaadia

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