In this paper, we study how to use the number of spike signals in a macaque’s motor cortex to estimate the position of its finger movement. First, we analyze the time correlation of a traditional state space model (SSM) and derive a convolutional space model (CSM) to decode the movement position of the macaque finger.
Brain–machine interface (BMI) researchers have traditionally focused on modeling endpoint reaching tasks to provide the control of neurally driven prosthetic arms. Most previous research has focused on achieving an endpoint control through a Cartesian-coordinate-centered approach.
A mathematical modeling for description of oscillation suppression by deep brain stimulation (DBS) is explored in this work. High frequency DBS introduced to the basal ganglia network can suppress pathological neural oscillations that occur in the Parkinsonian state.