A high-performance neural prosthesis enabled by control algorithm design

Abstract

Neural prostheses translate neural activity from the brain into control signals for guiding prosthetic devices, such as computer cursors and robotic limbs, and thus offer individuals with disabilities greater interaction with the world. However, relatively low performance remains a critical barrier to successful clinical translation; current neural prostheses are considerably slower, with less accurate control, than the native arm. Here we present a new control algorithm, the recalibrated feedback intention-trained Kalman filter (ReFIT-KF) that incorporates assumptions about the nature of closed-loop neural prosthetic control. When tested in rhesus monkeys implanted with motor cortical electrode arrays, the ReFIT-KF algorithm outperformed existing neural prosthetic algorithms in all measured domains and halved target acquisition time. This control algorithm permits sustained, uninterrupted use for hours and generalizes to more challenging tasks without retraining. Using this algorithm, we demonstrate repeatable high performance for years after implantation in two monkeys, thereby increasing the clinical viability of neural prostheses.

Figure 1. Performance comparison of native arm, ReFIT-KF, and Velocity-KF based cursor control

Native arm control shown in blue, ReFIT-KF in red, and Velocity-KF in green. All plots, except the cursor path traces, are constructed from 4 experimental days for each monkey on which all 3 control methods were tested. For each monkey and control method there are 545 to 659 center-out-and-back movements. (a) Representative traces of cursor path during center-out-and-back reaches. Dashed lines (not visible to the monkey) are the demand boxes for the eight peripheral targets and the central target, shown as translucent green circles. Targets alternated between the center and the peripheral in the sequence indicated by the numbers shown. Traces are continuous for the duration of all sixteen center-out-and-back movements, representing 15.27, 16.87, and 32.23 seconds of native arm, ReFIT-KF, and Velocity-KF reaching, respectively. (b) Bar graphs plotting maximum deviation from a straight-line path to the target on each successful trial (mean ± s.e.m). (c,d) Histograms of time to target for successful trials are shown as line graphs for monkeys J and L. The inset bar graphs plot the time to target (mean ± s.e.m). (e,f) Line graphs plotting the mean distance to the target as a function of time. The inset bar graph plots the mean ± s.e.m of the dial-in time, or the time required to finally settle on the demand box, after first acquired, to successfully hold for 500 ms. Hold time is not included in the dial-in time. The thickened portion of the line graphs also indicate dial-in time, beginning at the mean time of first target acquire, and ending at mean trial duration minus 500ms. These data are from experiments J-2010-10-27, J-2010-10-28, J-2010-10-29, J-2010-11-02, L-2010-10-27, L-2010-10-28, L-2010-10-29, and L-2010-11-02.

Figure 2. Performance of ReFIT-KF control across 4 years

Performance is measured by the Fitts’s law metric (Supplementary Modeling). Data from monkey J and monkey L are shown as 98 orange circles and 182 cyan squares, respectively. Each point plots the performance of the ReFIT-KF algorithm trained on that experimental day. The eight filled data points (four for each monkey) are calculated from the same datasets used to generate Figure 1. Linear regression lines for data from monkey J (orange) and monkey L (cyan) are shown. For all datasets shown, the trial success rate was >90%. Additional details on these data are summarized in Supplementary Table 2.

Figure 3. Performance comparison of native arm vs. ReFIT-KF for the pinball task

Native arm control is in blue, ReFIT-KF in red. In this task, each target location is selected from a uniform distribution spanning the workspace. (a) Each column shows data from 20-minute segments. The top rows are randomly selected cursor traces for 4 subsequent target acquisitions. Target demand boxes are shown as dashed lines and target sequence is indicated from 0 to 4. The bottom row shows normalized histograms of time to target for successful trials. Arrows below the plot indicate average time. (b) Target acquisition rate per minute throughout the sessions is shown. The sharp rate drop indicates when the monkey lost interest in the task. A histogram of acquisition rate across the sessions is inset. The native arm and ReFIT-KF sessions (L-2010-04-01 and L-2010-04-12) were on two separate days, within 11 days of each other, when the monkey demonstrated a high degree of motivation.

Figure 4. Performance comparison of native arm vs. ReFIT-KF for the obstacle avoidance task

Native arm control shown in blue, ReFIT-KF in red. In this task the monkey had to move the cursor from the initial target (labeled 0) to the final target (labeled 1, demand box shown as dashed line) without hitting the magenta-colored barrier. One representative cursor trace is shown from each of the four principle observed movement types: curve under, curve over, straight (no barrier), and collision into barrier. These data are from experiment J-2010-03-09.

Figure 5. Illustrations of the online neural control paradigm and the ReFIT-KF training methodology

(a) The input to the control algorithm at time i is a vector of spike counts, y_t, from implanted electrodes. Y_t is translated into a velocity output, v_t, to drive the cursor. (b) ReFIT-KF is trained in two-stages. Initially, cursor kinematics and neural activity are collected during arm control or during an observation phase in which cursor movement is automated. These arm movement or observed cursor kinematics are regressed against neural activity to generate an initial control algorithm. Then, a new set of cursor kinematics and neural activity are collected using the initial algorithm in closed loop. The kinematics collected during neural control (red vectors) are used to estimate intention by rotating the velocities towards the goal (blue vectors). This estimate of intended kinematics is regressed against neural activity to generate and run ReFIT-KF.