The science of neural interface systems

Abstract

The ultimate goal of neural interface research is to create links between the nervous system and the outside world either by stimulating or by recording from neural tissue to treat or assist people with sensory, motor, or other disabilities of neural function. Although electrical stimulation systems have already reached widespread clinical application, neural interfaces that record neural signals to decipher movement intentions are only now beginning to develop into clinically viable systems to help paralyzed people. We begin by reviewing state-of-the-art research and early-stage clinical recording systems and focus on systems that record single-unit action potentials. We then address the potential for neural interface research to enhance basic scientific understanding of brain function by offering unique insights in neural coding and representation, plasticity, brain-behavior relations, and the neurobiology of disease. Finally, we discuss technical and scientific challenges faced by these systems before they are widely adopted by severely motor-disabled patients.

Figure 1

The four components of a closed-loop, neural interface system: (1) a recording array that extracts neural signals, (2) a decoding algorithm that translates these neural signals into a set of command signals, (3)an output device that is controlled by these command signals, and (4) sensory feedback in the form of vision and potentially other sensory modalities. Transparent head image is courtesy of ©iStockphoto.com/Kiyoshi Takahase Segundo.

Figure 2

(a) A schematic of a typical electrophysiological experiment investigating the cortical correlates of motor learning. An electrode records changes in neural activity from a cortical area that (A1) may not be causally related to behavioral changes observed in behavioral output or (A2) may not be directly connected with the behavioral output. (b) A schematic of a typical neural interface experiment in which a multielectrode array records signals from the same cortical areas as in a. In this case, changes in neural activity are causally related to the output behavior of the device being controlled by the neural interface.

Figure 3

(a) An NIS paradigm in which kinematic parameters are decoded and sent to either a virtual object or a physical plant, both of which passively follow the kinematic commands. In the case of a physical plant, a proportional-derivative (PD) controller parameterized by a stiffness (K) and viscosity (B) generates a force or torque signal such that the plant is forced to follow the desired kinematics. (b) An NIS paradigm in which kinetic parameters are decoded and act as control variables on a dynamic system.