Velocity neurons improve performance more than goal or position neurons do in a simulated closed-loop BCI arm-reaching task.

James Y Liao,Robert F Kirsch

doi:10.3389/fncom.2015.00084

Abstract

Brain-Computer Interfaces (BCIs) that convert brain-recorded neural signals into intended movement commands could eventually be combined with Functional Electrical Stimulation to allow individuals with Spinal Cord Injury to regain effective and intuitive control of their paralyzed limbs. To accelerate the development of such an approach, we developed a model of closed-loop BCI control of arm movements that (1) generates realistic arm movements (based on experimentally measured, visually-guided movements with real-time error correction), (2) simulates cortical neurons with firing properties consistent with literature reports, and (3) decodes intended movements from the noisy neural ensemble. With this model we explored (1) the relative utility of neurons tuned for different movement parameters (position, velocity, and goal) and (2) the utility of recording from larger numbers of neurons—critical issues for technology development and for determining appropriate brain areas for recording. We simulated arm movements that could be practically restored to individuals with severe paralysis, i.e., movements from an armrest to a volume in front of the person. Performance was evaluated by calculating the smallest movement endpoint target radius within which the decoded cursor position could dwell for 1 s. Our results show that goal, position, and velocity neurons all contribute to improve performance. However, velocity neurons enabled smaller targets to be reached in shorter amounts of time than goal or position neurons. Increasing the number of neurons also improved performance, although performance saturated at 30–50 neurons for most neuron types. Overall, our work presents a closed-loop BCI simulator that models error corrections and the firing properties of various movement-related neurons that can be easily modified to incorporate different neural properties. We anticipate that this kind of tool will be important for development of future BCIs.

Highlights

These simulations indicated that increases in the number of both goal neurons and position-velocity neurons contributed to increased performance for target radii greater than 3 cm
We have developed a model of a closed-loop Brain-Computer Interfaces (BCIs) for controlling human arm movements that uses a model of point-to-point arm movement trajectories, constructs an ensemble of movement-related cortical neurons that encode these ideal movements using realistic firing characteristics and noise properties based on literature reports, and decodes the movement commands that would be expected from a practical decoder
Position, and velocity-tuned neurons all strongly contributed to decrease the size of targets that could be reached by a simulated BCI

Summary

Introduction

Brain-Computer Interfaces (BCI) are systems that record electrical signals from the brain and relate information in these signals to intended actions such as arm movements (Bansal et al, 2012; Hochberg et al, 2012; Collinger et al, 2013; Nakanishi et al, 2013) or communication (Santhanam et al, 2006; Krusienski and Wolpaw, 2009). Many studies, including Collinger et al (2013) and Hochberg et al (2012), assume that firing rates of individual neurons recorded from primary motor cortex are linearly related to the kinematics of the robot hand. They assume that the neurons are directionally cosine-tuned and gain-modulated by the magnitude of the kinematic variable (Kettner et al, 1988; Schwartz et al, 1988; Moran and Schwartz, 1999; Wang et al, 2007). A recent study showed that human parietal cortex contains goal-tuned neurons that can perform a closed-loop goal selection task (Aflalo et al, 2015)

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