FaNeuRobot: A Framework for Robot and Prosthetics Control Using the NeuCube Spiking Neural Network Architecture and Finite Automata Theory

Abstract

Limb amputation is a global problem. Prosthetic limbs can enhance the quality of life of amputees. To this end, anthropomorphic design and intuitive manipulation are two essential requirements. This paper presents a motor control framework for prosthetic control through Brain-Machine Interface (BMI) using Finite Automata Theory, and NeuCube Evolving Spiking Neural Network (SNN) architecture. Voluntary control of prosthetics requires decoding motor commands from the Central Nervous System of the amputee. Selection of the most suitable biomedical signal depends on many parameters such as level of amputation and muscle atrophy. Non-invasive BMI's have the possibility of supporting a wider range of amputees as it extracts the motor commands from the brain. In this paper, we present a proof of concept study on whether a cognitive computational model that is inspired by the motor control of the human body through muscle synergies combined with an anthropomorphic mechanical design, can result in accurate and robust prosthetic control through a noninvasive BMI. In future, learning of a complex Finite Automata that reflects complex upper limb motor behaviours will be investigated.