Selection and parameterization of cortical neurons for neuroprosthetic control

Abstract

When designing neuroprosthetic interfaces for motor function, it is crucial to have a system that can extract reliable information from available neural signals and produce an output suitable for real life applications. Systems designed to date have relied on establishing a relationship between neural discharge patterns in motor cortical areas and limb movement, an approach not suitable for patients who require such implants but who are unable to provide proper motor behavior to initially tune the system. We describe here a method that allows rapid tuning of a population vector-based system for neural control without arm movements. We trained highly motivated primates to observe a 3D center-out task as the computer played it very slowly. Based on only 10-12 s of neuronal activity observed in M1 and PMd, we generated an initial mapping between neural activity and device motion that the animal could successfully use for neuroprosthetic control. Subsequent tunings of the parameters led to improvements in control, but the initial selection of neurons and estimated preferred direction for those cells remained stable throughout the remainder of the day. Using this system, we have observed that the contribution of individual neurons to the overall control of the system is very heterogeneous. We thus derived a novel measure of unit quality and an indexing scheme that allowed us to rate each neuron's contribution to the overall control. In offline tests, we found that fewer than half of the units made positive contributions to the performance. We tested this experimentally by having the animals control the neuroprosthetic system using only the 20 best neurons. We found that performance in this case was better than when the entire set of available neurons was used. Based on these results, we believe that, with careful task design, it is feasible to parameterize control systems without any overt behaviors and that subsequent control system design will be enhanced with cautious unit selection. These improvements can lead to systems demanding lower bandwidth and computational power, and will pave the way for more feasible clinical systems.

General-Purpose Filter Design for Neural Prosthetic Devices

“…In the current brain-driven interface prototypes, parameters of the observation model that relate neural activity to the user's intentions are learned by the algorithm during a training session consisting of real, imagined, or viewed actions (Carmena et al 2003;Leuthardt et al 2004;Musallam et al 2004;Serruya et al 2002;Shenoy et al 2003;Taylor et al 2002;Wahnoun et al 2006;Wolpaw and McFarland 2004). In practice, this facilitates rapid (also termed "instant") improvements in device performance because the device learns to match the user, whereas the user can maintain their operating assumptions about the device.…”

“…Although neural activity parameters are commonly fit in prosthesis applications using separate intervals of training data (Carmena et al 2003;Hochberg et al 2006;Leuthardt et al 2004;Musallam et al 2004;Santhanam et al 2006;Serruya et al 2002;Taylor et al 2002;Wahnoun et al 2006;Wolpaw and McFarland 2004), these parameters can also be adjusted on the fly by simultaneously estimating the user's intentions and neural activity parameters (Eden et al 2004a;Eden et al 2004b). In a more modular "lock-step" implementation, the user's intentions are estimated, followed by refinements of the neural activity parameter estimates, but these actions are alternated at every time step.…”

“…Neural prosthetic device algorithms are assessed a variety of ways, including simulated off-line decoding (Eden et al 2004a,b;Kemere and Meng 2005;Kemere et al 2004;Srinivasan and Brown 2007;Srinivasan et al 2005Srinivasan et al , 2006, off-line decoding of actual neural activity recorded during movements in laboratory animals (Brockwell et al 2004;Hatsopoulos et al 2004;Wessberg et al 2000), and on-line prototypes involving laboratory animals or humans (Carmena et al 2003;Hochberg et al 2006;Leuthardt et al 2004;Musallam et al 2004;Serruya et al 2002;Shenoy et al 2003;Taylor et al 2002;Wahnoun et al 2006;Wolpaw and McFarland 2004;Santhanam et al 2006). One ultimate gold standard in comparing methods is a double-blinded longitudinal study of device performance in activities of daily living for a population of individuals that experience well-defined stages and pathological mechanisms of a target motor deficit.…”

Biomimetic Brain Machine Interfaces for the Control of Movement

“…Others have shown that real-time decoding parameters can be computed from neural recordings made while monkeys passively observe the corresponding movements (Wahnoun et al, 2006) or when paralyzed patients imagine tracking a moving cursor (Hochberg et al, 2006). Hatsopoulos and his colleagues have compared the performance of such observationbased decoders with that of movement-based decoders in providing real-time control.…”

Congruent Activity during Action and Action Observation in Motor Cortex

“…At the single-cell level, observation-related MI activity has only been used to set the initial parameters for motor decoding schemes (Hochberg et al, 2006;Wahnoun et al, 2006), but the origin, extent, and character of this activity has not been described previously.…”