Neural Prostheses: Linking Brain Signals to Prosthetic Devices Carlos Pedreira * , Juan Martinez * and Rodrigo Quian Quiroga Department of Engineering, University of Leicester, Leicester, United Kingdom * These authors contributed equally. (Tel : +44-116-252-2872; E-mail: cp155@le.ac.uk) Abstract: This paper discusses Neuroprosthetic applications for potential use by paralyzed patients or amputees. These systems require advanced processing of neural signals to drive the prosthetic devices using decoding algorithms. The possibility of predicting motor commands from neural signals are the core of neural prosthetic devices and along this line we show how it is possible to predict movement intentions as well as what subjects are seeing from the firing of population of neurons. Keywords: Neural Prostheses, decoding, neuroscience, robotics, control theory. 1. INTRODUCTION Millions of people are paralyzed or have suffered an amputation. Although these people can still see the object they may want to reach, for example a glass of wine, and can still process in their brains the specific commands to pursue this goal, the action cannot be completed due to, for example, a spinal cord injury or due to the fact that the arm has been amputated. Given that in most cases the brain of these persons is intact, the possibility of reading brain signals would allow the development of Neuroprosthetic devices, such as a robot arm that is driven by neural activity. Reaching for an object involves a series of complex processes in the brain (see Fig. 1), from evaluation of visual inputs to motor planning and execution. Converging evidence from monkey neurophysiology has shown that the posterior parietal cortex (PPC) is a key node in this process, being involved in different types of movement plans [1, 2]. In fact, the PPC lies between the primary visual areas in the occipital lobe and the motor cortex, thus having a privileged location for visuo-motor transformations. Given this evidence, as well as related findings from motor cortex, the question arises of whether it is possible to predict movement plans from the activity of these neurons. Several studies have shown that this is the case, and the possibility of such predictions encouraged researchers to work on the development of Neural Prostheses [1-10]. In this paper we describe in detail the main steps for Neuroprosthetic implementations. We start by describing how to process the data for extracting the activity of single neurons using spike sorting algorithms and continue with the description of decoding algorithms Fig. 1 Diagram of a neural prosthetic system.