A New Brain-Machine Interface Algorithm Based on Neural Firing: A Study Based on Modeling

   In this paper, we develop a novel approach for bidirectional brain-machine interface (BMI). First, we propose a neural network model for the sensory cortex (S۱) connected to the neural network model of the motor cortex (M۱) considering the topographic mapping between S۱ and M۱. We use the ۴-box model in S۱ and ۴-box in M۱ so that each box contains ۵۰۰ neutrons. Individual boxes are composed of two neural populations: inhibitory interneurons and pyramidal neurons. Next, we develop a new BMI algorithm based on neural firing. The main concept of these BMI algorithms is to close the loop between two components: the sensory interface and the motor interface. The sensory interface encodes some of the state parameters of the external device into an electrical stimulus delivered to the S۱ model. The motor interface takes neural recordings from the M۱ model and decodes them into a force applied to the object. We present the simulation results for the on line BMI which means that there is a real-time information exchange between the S۱-M۱ network model and the external device.

Keywords:

Brain machine interface , Neural firing , Sensory and motor cortex