Francis R Willett

Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration

SUMMARY Background People with chronic tetraplegia due to high cervical spinal cord injury (SCI) can regain limb movements through coordinated electrical stimulation of peripheral muscles and nerves, known as Functional Electrical Stimulation (FES). Users typically command FES systems through other preserved, but limited and unrelated, volitional movements (e.g. facial muscle activity, head movements). We demonstrate an individual with traumatic high cervical SCI performing coordinated reaching and grasping movements using his own paralyzed arm and hand, reanimated through FES, and commanded using his own cortical signals through an intracortical brain-computer-interface (iBCI). Methods The study participant (53 years old, C4, ASIA A) received two intracortical microelectrode arrays in the hand area of motor cortex, and 36 percutaneous electrodes for electrically stimulating hand, elbow, and shoulder muscles. The participant used a motorized mobile arm support for gravitational assistance and to provide humeral ab/adduction under cortical control. We assessed the participant’s ability to cortically command his paralyzed arm to perform simple single-joint arm/hand movements and functionally meaningful multi-joint movements. We compared iBCI control of his paralyzed arm to that of a virtual 3D arm. This study is registered with ClinicalTrials.gov, NCT00912041. Findings The participant successfully cortically commanded single-joint and coordinated multi-joint arm movements for point-to-point target acquisitions (80% – 100% accuracy) using first a virtual arm, and second his own arm animated by FES. Using his paralyzed arm, the participant volitionally performed self-paced reaches to drink a mug of coffee (successfully completing 11 of 12 attempts within a single session) and feed himself. Interpretation This is the first demonstration of a combined FES+iBCI neuroprosthesis for both reaching and grasping for people with SCI resulting in chronic tetraplegia, and represents a major advance, with a clear translational path, for clinically viable neuroprostheses for restoring reaching and grasping post-paralysis.BACKGROUND People with chronic tetraplegia, due to high-cervical spinal cord injury, can regain limb movements through coordinated electrical stimulation of peripheral muscles and nerves, known as functional electrical stimulation (FES). Users typically command FES systems through other preserved, but unrelated and limited in number, volitional movements (eg, facial muscle activity, head movements, shoulder shrugs). We report the findings of an individual with traumatic high-cervical spinal cord injury who coordinated reaching and grasping movements using his own paralysed arm and hand, reanimated through implanted FES, and commanded using his own cortical signals through an intracortical brain-computer interface (iBCI). METHODS We recruited a participant into the BrainGate2 clinical trial, an ongoing study that obtains safety information regarding an intracortical neural interface device, and investigates the feasibility of people with tetraplegia controlling assistive devices using their cortical signals. Surgical procedures were performed at University Hospitals Cleveland Medical Center (Cleveland, OH, USA). Study procedures and data analyses were performed at Case Western Reserve University (Cleveland, OH, USA) and the US Department of Veterans Affairs, Louis Stokes Cleveland Veterans Affairs Medical Center (Cleveland, OH, USA). The study participant was a 53-year-old man with a spinal cord injury (cervical level 4, American Spinal Injury Association Impairment Scale category A). He received two intracortical microelectrode arrays in the hand area of his motor cortex, and 4 months and 9 months later received a total of 36 implanted percutaneous electrodes in his right upper and lower arm to electrically stimulate his hand, elbow, and shoulder muscles. The participant used a motorised mobile arm support for gravitational assistance and to provide humeral abduction and adduction under cortical control. We assessed the participants ability to cortically command his paralysed arm to perform simple single-joint arm and hand movements and functionally meaningful multi-joint movements. We compared iBCI control of his paralysed arm with that of a virtual three-dimensional arm. This study is registered with ClinicalTrials.gov, number NCT00912041. FINDINGS The intracortical implant occurred on Dec 1, 2014, and we are continuing to study the participant. The last session included in this report was Nov 7, 2016. The point-to-point target acquisition sessions began on Oct 8, 2015 (311 days after implant). The participant successfully cortically commanded single-joint and coordinated multi-joint arm movements for point-to-point target acquisitions (80-100% accuracy), using first a virtual arm and second his own arm animated by FES. Using his paralysed arm, the participant volitionally performed self-paced reaches to drink a mug of coffee (successfully completing 11 of 12 attempts within a single session 463 days after implant) and feed himself (717 days after implant). INTERPRETATION To our knowledge, this is the first report of a combined implanted FES+iBCI neuroprosthesis for restoring both reaching and grasping movements to people with chronic tetraplegia due to spinal cord injury, and represents a major advance, with a clear translational path, for clinically viable neuroprostheses for restoration of reaching and grasping after paralysis. FUNDING National Institutes of Health, Department of Veterans Affairs.

Improving brain-machine interface performance by decoding intended future movements

OBJECTIVE A brain-machine interface (BMI) records neural signals in real time from a subjects brain, interprets them as motor commands, and reroutes them to a device such as a robotic arm, so as to restore lost motor function. Our objective here is to improve BMI performance by minimizing the deleterious effects of delay in the BMI control loop. We mitigate the effects of delay by decoding the subjects intended movements a short time lead in the future. APPROACH We use the decoded, intended future movements of the subject as the control signal that drives the movement of our BMI. This should allow the users intended trajectory to be implemented more quickly by the BMI, reducing the amount of delay in the system. In our experiment, a monkey (Macaca mulatta) uses a future prediction BMI to control a simulated arm to hit targets on a screen. MAIN RESULTS Results from experiments with BMIs possessing different system delays (100, 200 and 300 ms) show that the monkey can make significantly straighter, faster and smoother movements when the decoder predicts the users future intent. We also characterize how BMI performance changes as a function of delay, and explore offline how the accuracy of future prediction decoders varies at different time leads. SIGNIFICANCE This study is the first to characterize the effects of control delays in a BMI and to show that decoding the users future intent can compensate for the negative effect of control delay on BMI performance.

High performance communication by people with paralysis using an intracortical brain-computer interface

Brain-computer interfaces (BCIs) have the potential to restore communication for people with tetraplegia and anarthria by translating neural activity into control signals for assistive communication devices. While previous pre-clinical and clinical studies have demonstrated promising proofs-of-concept (Serruya et al., 2002; Simeral et al., 2011; Bacher et al., 2015; Nuyujukian et al., 2015; Aflalo et al., 2015; Gilja et al., 2015; Jarosiewicz et al., 2015; Wolpaw et al., 1998; Hwang et al., 2012; Spüler et al., 2012; Leuthardt et al., 2004; Taylor et al., 2002; Schalk et al., 2008; Moran, 2010; Brunner et al., 2011; Wang et al., 2013; Townsend and Platsko, 2016; Vansteensel et al., 2016; Nuyujukian et al., 2016; Carmena et al., 2003; Musallam et al., 2004; Santhanam et al., 2006; Hochberg et al., 2006; Ganguly et al., 2011; O’Doherty et al., 2011; Gilja et al., 2012), the performance of human clinical BCI systems is not yet high enough to support widespread adoption by people with physical limitations of speech. Here we report a high-performance intracortical BCI (iBCI) for communication, which was tested by three clinical trial participants with paralysis. The system leveraged advances in decoder design developed in prior pre-clinical and clinical studies (Gilja et al., 2015; Kao et al., 2016; Gilja et al., 2012). For all three participants, performance exceeded previous iBCIs (Bacher et al., 2015; Jarosiewicz et al., 2015) as measured by typing rate (by a factor of 1.4–4.2) and information throughput (by a factor of 2.2–4.0). This high level of performance demonstrates the potential utility of iBCIs as powerful assistive communication devices for people with limited motor function. Clinical Trial No: NCT00912041 DOI: http://dx.doi.org/10.7554/eLife.18554.001

Continuous decoding of intended movements with a hybrid kinetic and kinematic brain machine interface

Although most brain-machine interface (BMI) studies have focused on decoding kinematic parameters of motion, it is known that motor cortical activity also correlates with kinetic signals, including hand force and joint torque. In this experiment, a monkey used a cortically-controlled BMI to move a visual cursor and hit a sequence of randomly placed targets. By varying the contributions of separate kinetic and kinematic decoders to the movement of a virtual arm, we evaluated the hypothesis that a BMI incorporating both signals (Hybrid BMI) would outperform a BMI decoding kinematic information alone (Position BMI). We show that the trajectories generated by the Hybrid BMI during real-time decoding were straighter and smoother than those of the Position BMI. These results may have important implications for BMI applications that require controlling devices with inherent, physical dynamics or applying forces to the environment.

Online adaptive decoding of intended movements with a hybrid kinetic and kinematic brain machine interface

Traditional brain machine interfaces for control of a prosthesis have typically focused on the kinematics of movement, rather than the dynamics. BMI decoders that extract the forces and/or torques to be applied by a prosthesis have the potential for giving the patient a much richer level of control across different dynamic scenarios or even scenarios in which the dynamics of the limb/environment are changing. However, it is a challenge to train a decoder that is able to capture this richness given the small amount of calibration data that is usually feasible to collect a priori. In this work, we propose that kinetic decoders should be continuously calibrated based on how they are used by the subject. Both intended hand position and joint torques are decoded simultaneously as a monkey performs a random target pursuit task. The deviation between intended and actual hand position is used as an estimate of error in the recently decoded joint torques. In turn, these errors are used to drive a gradient descent algorithm for improving the torque decoder parameters. We show that this approach is able to quickly restore the functionality of a torque decoder following substantial corruption with Gaussian noise.

Relationship between microelectrode array impedance and chronic recording quality of single units and local field potentials

Practical application of intracortical microelectrode technology is currently hindered by the inability to reliably record neuronal signals chronically. The precise mechanism of device failure is still under debate, but most likely includes some combination of tissue reaction, mechanical failure, and chronic material degradation. Impedance is a measure of the ease with which current flows through a working electrode under a driving voltage. Impedance has been hypothesized to provide information about an electrodes surrounding tissue reaction as well as chronic insulation degradation. In this study, we investigated the relationship between an electrodes impedance and its chronic recording performance as measured by the number of isolatable single units and the quality of local field potential recordings. Two 64-channel electrode arrays implanted in separate monkeys were assessed. We found no simple relationship between impedance and recording quality that held for both animals across all time periods. This suggests that future investigations on the topic should adopt a more fine-grained within-day and within-animal analysis. We also found new evidence from local field potential spatial correlation supporting the theory that insulation degradation is an important contributor to electrode failure.

Rapid calibration of an intracortical brain-computer interface for people with tetraplegia

OBJECTIVE Brain-computer interfaces (BCIs) can enable individuals with tetraplegia to communicate and control external devices. Though much progress has been made in improving the speed and robustness of neural control provided by intracortical BCIs, little research has been devoted to minimizing the amount of time spent on decoder calibration. APPROACH We investigated the amount of time users needed to calibrate decoders and achieve performance saturation using two markedly different decoding algorithms: the steady-state Kalman filter, and a novel technique using Gaussian process regression (GP-DKF). MAIN RESULTS Three people with tetraplegia gained rapid closed-loop neural cursor control and peak, plateaued decoder performance within 3 min of initializing calibration. We also show that a BCI-naïve user (T5) was able to rapidly attain closed-loop neural cursor control with the GP-DKF using self-selected movement imagery on his first-ever day of closed-loop BCI use, acquiring a target 37 s after initiating calibration. SIGNIFICANCE These results demonstrate the potential for an intracortical BCI to be used immediately after deployment by people with paralysis, without the need for user learning or extensive system calibration.

Differences in motor cortical representations of kinematic variables between action observation and action execution and implications for brain-machine interfaces.

Observing an action being performed and executing the same action cause similar patterns of neural activity to emerge in the primary motor cortex (MI). Previous work has shown that the neural activity evoked during action observation (AO) is informative as to both the kinematics and muscle activation patterns of the action being performed, although the neural activity recorded during action observation contains less information than the activity recorded during action execution (AE). In this study, we extend these results by comparing the representation of different kinematic variables in MI single /multi unit activity between AO and AE conditions in three rhesus macaques. We show that the representation of acceleration decreases more significantly than that of position and velocity in AO (population decoding performance for acceleration decreases more steeply, and fewer neurons in AO encode acceleration significantly as compared to AE). We discuss the relevance of these results to brain-machine interfaces that make use of neural activity during AO to initialize a mapping function between neural activity and motor commands.