High-performance neuroprosthetic control by an individual with tetraplegia

Abstract

Background: Paralysis or amputation of an arm results in the loss of the ability to orient the hand and grasp, manipulate, and carry objects, functions that are essential for activities of daily living. Brain-machine interfaces could provide a solution to restoring many of these lost functions. We therefore tested whether an individual with tetraplegia could rapidly achieve neurological control of a high-performance prosthetic limb using this type of an interface.

Methods: We implanted two 96-channel intracortical microelectrodes in the motor cortex of a 52-year-old individual with tetraplegia. Brain-machine-interface training was done for 13 weeks with the goal of controlling an anthropomorphic prosthetic limb with seven degrees of freedom (three-dimensional translation, three-dimensional orientation, one-dimensional grasping). The participant's ability to control the prosthetic limb was assessed with clinical measures of upper limb function. This study is registered with ClinicalTrials.gov, NCT01364480.

Findings: The participant was able to move the prosthetic limb freely in the three-dimensional workspace on the second day of training. After 13 weeks, robust seven-dimensional movements were performed routinely. Mean success rate on target-based reaching tasks was 91·6% (SD 4·4) versus median chance level 6·2% (95% CI 2·0-15·3). Improvements were seen in completion time (decreased from a mean of 148 s [SD 60] to 112 s [6]) and path efficiency (increased from 0·30 [0·04] to 0·38 [0·02]). The participant was also able to use the prosthetic limb to do skilful and coordinated reach and grasp movements that resulted in clinically significant gains in tests of upper limb function. No adverse events were reported.

Interpretation: With continued development of neuroprosthetic limbs, individuals with long-term paralysis could recover the natural and intuitive command signals for hand placement, orientation, and reaching, allowing them to perform activities of daily living.

Funding: Defense Advanced Research Projects Agency, National Institutes of Health, Department of Veterans Affairs, and UPMC Rehabilitation Institute.

Conflict of interest statement

Conflict of interest statement

MV and AS have a patent application pending that covers some of the methodology used in this study. We declare that we have no other conflicts of interest.

Copyright © 2013 Elsevier Ltd. All rights reserved.

Figures

Figure 1. Array location and experiment setup

(A) Pre-operative functional magnetic resonance imaging (fMRI) activation maps on a subject-specific brain model during video-guided attempted movement. The colored activation maps represent blood-oxygenation level dependent (BOLD) activity during video-guided attempted movements: yellow=sequential finger flexion, red=hand grasping, blue=shoulder shrug, green=lip pursing. Approximate array locations are shown as black squares on the inset figure. The arrays were implanted over motor cortex anterior to the central sulcus (CS) approximately 14 mm apart. (B) MPL and 7D Sequence Task setup. The subject was not presented with physical targets. Instead, LEDs (indicated by the white arrow) were used to instruct the participant to hit the “near” (0·35 m from the shoulder) or “far” (0·52 m from the shoulder) translation target corresponding to one of the white circles on the board in front of the MPL. Orientation and grasp targets were presented by a computer-generated verbal command. (C) Diagram of the MPL and translation targets for the 7D Sequence Task. The MPL coordinate system is shown centered at the shoulder. Translation targets had an 8 cm radius and the MPL endpoint (center of the palm) had to be within this region for a successful trial. The MPL endpoint also had to be within the translation target success region in order to successfully achieve the orientation (± 15 degrees) and grasp the targets that were given as audio cues. The time-out period was set to 10 seconds.

Figure 2. Number of units over time

The top set of data points (blue dots) indicates the number of units recorded during BMI sessions conducted 10–98 days post-implant. Starting at Day 24, the number of units increased linearly (y = 0·368x + 210·0, R2 = 0·356, p<0·001). The bottom set of data points (red squares) indicates the number of units tuned to movement velocity (Eq. 1, R2>0·1), which increased linearly over the duration of recording (y = 0·438x + 24·3, R2 = 0·155, p<0·01). For reference, 4D training began on Day 24 and 7D training began on Day 32.

Figure 3. Summary of 7D brain-control performance

(A) The solid black dots indicate the subject’s success rate on the 7D Sequence Task for each block (20 trials) of 7D brain-control training. Blocks that the subject performed with various levels of ortho-impedance or stabilizing assist are shown as open circles and squares respectively. After Day 66 post-implant, all reported performance data were collected using full brain-control with no computer assistance. The red dots indicate the median chance level for each day with the 95% confidence interval (5th and 95th percentiles from 200 simulations per trial) shown as error bars. (B) Normalized performance index for each day of 7D brain control. For each block of 20 trials, the success rate was normalized to the median chance level. Performance increased exponentially over time as described by the equation y = 1·812e0·04(x-32) (R2 = 0·114, p=0·001), where x is the number of days post-implant. For subplots C-E, each dot represents the mean block time or path efficiency for one block of 20 trials of the 7D sequence task completed by the participant. Linear fits to the participant’s data are shown as a red line. The mean block time or path efficiency of the MPL under auto-control is shown as a solid horizontal black line. (C) Block completion time, not including the presentation phase time, decreased linearly over the testing period (y = −0·632x + 171·4, R2 = 0·142, p<0·001). (D) Average 7D path efficiency of the MPL under brain-control increased linearly over time (y = 0·001x + 0·228, R2 = 0·160, p<0·001). (E) The greatest improvement in path efficiency occurred in the translation dimensions. Path efficiency for 3D translation during the 7D sequence task increased linearly over time (y = 0·003x + 0·317, R2 = 0·165, p<0·001). Only successful trials were included in the calculation of path efficiency. For all equations, x is the number of days post-implant.

Figure 4. MPL position, orientation, and grasp aperture during four 7D Sequence Task trials under full brain control

A thick black horizontal bar denotes whether a translation, orientation, or grasp target was being attempted although the participant had control of all 7 dimensions at all times. Each new translation target indicates the start of a new trial, which is also marked with an arrow along the time axis. MPL kinematics as controlled by the participant are shown as solid lines. The target position for each dimension is shown as a dotted line. Grey shaded regions indicate presentation phases in which the MPL was paused and the subject was listening to a computer-generated verbal command. A grasp aperture of 1 indicates that the hand was fully closed. The participant was successful in maintaining position in one control domain while changing position in another, as instructed. Webvideo 1 shows the subject’s performance with the MPL in these 4 trials.

Figure 5. Changes in neural tuning over time

(A) Fraction of units whose firing rate predicted 7D MPL endpoint velocity (Eq. 1) with an R2>0·10 on each day of 7D brain-control training. Each dot represents data from a single decoder. On a single day decoders were trained using observation data and brain-control data with ortho-impedance. The percentage of units tuned to 7D MPL velocity trended towards a linear increase over the training period (y = 0·002x + 0·100, R2 = 0·119, p=0·066). (B) Percentage of units significantly tuned to MPL kinematics for early training (Weeks 5–8, Blue) of 7D BMI training compared to late training (Weeks 11–14, Red). Each bar is centered between the upper and lower bound of R2 values for a given bin. All R2>0·5 were combined into a single bin. Over time, the percentage of units with an R2<0·1 decreased, while the percentage of units tuned to kinematics with 0·1≤R2≤0·5 increased.