Modeling the Relation from Motor Cortical Neuronal Firing to Hand Movements Using Competitive Linear Filters and a MLP
Recent research has demonstrated that linear models are able to estimate hand positions using populations of action potentials collected in the pre-motor and motor cortical areas of a primate's brain. One of the applications of this result is to restore movement in patients suffering from paralysis. To implement this technology in real-time, reliable and accurate signal processing models that produce sufficiently small error in the estimated hand positions are required. In this paper, we propose the hybrid model approach that combines competitive linear filters with a neural network. The mapping performance of our approach is compared with a single Wiener filter during reaching movements. Our approach demonstrates more accurate estimations.
