Brain-Machine Interface control of a robot arm using actor-critic rainforcement learning.
Here we demonstrate how a marmoset monkey can use a reinforcement learning (RL) Brain-Machine Interface (BMI) to effectively control the movements of a robot arm for a reaching task. In this work, an actor-critic RL algorithm used neural ensemble activity in the monkey's motor cortext to control the robot movements during a two-target decision task. This novel approach to decoding offers unique advantages for BMI control applications. Compared to supervised learning decoding methods, the actor-critic RL algorithm does not require an explicit set of training data to create a static control model, but rather it incrementally adapts the model parameters according to its current performance, in this case requiring only a very basic feedback signal. We show how this algorithm achieved high performance when mapping the monkey's neural states (94%) to robot actions, and only needed to experience a few trials before obtaining accurate real-time control of the robot arm. Since RL methods responsively adapt and adjust their parameters, they can provide a method to create BMIs that are robust against perturbations caused by changes in either the neural input space or the output actions they generate under different task requirements or goals.
Department of Biomedical Engineering, Miami University, Coral Gables, Fl 33146, USA.
Eric A
EA
Pohlmeyer
Babak
B
Mahmoudi
Shijia
S
Geng
Noeline
N
Prins
Justin C
JC
Sanchez
D000465:Algorithms; D000818:Animals; D001132:Arm; D001676:Biofeedback, Psychology / Q000379:methods / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D062207:Brain-Computer Interfaces; D002144:Callithrix; D005103:Expert Systems; D008328:Man-Machine Systems; D012054:Reinforcement, Psychology; D012371:Robotics / Q000379:methods*; D013647:Task Performance and Analysis