[show abstract][hide abstract] ABSTRACT: Functional electrical stimulation (FES), the coordinated electrical activation of multiple muscles, has been used to restore arm and hand function in people with paralysis. User interfaces for such systems typically derive commands from mechanically unrelated parts of the body with retained volitional control, and are unnatural and unable to simultaneously command the various joints of the arm. Neural interface systems, based on spiking intracortical signals recorded from the arm area of motor cortex, have shown the ability to control computer cursors, robotic arms and individual muscles in intact non-human primates. Such neural interface systems may thus offer a more natural source of commands for restoring dexterous movements via FES. However, the ability to use decoded neural signals to control the complex mechanical dynamics of a reanimated human limb, rather than the kinematics of a computer mouse, has not been demonstrated. This study demonstrates the ability of an individual with long-standing tetraplegia to use cortical neuron recordings to command the real-time movements of a simulated dynamic arm. This virtual arm replicates the dynamics associated with arm mass and muscle contractile properties, as well as those of an FES feedback controller that converts user commands into the required muscle activation patterns. An individual with long-standing tetraplegia was thus able to control a virtual, two-joint, dynamic arm in real time using commands derived from an existing human intracortical interface technology. These results show the feasibility of combining such an intracortical interface with existing FES systems to provide a high-performance, natural system for restoring arm and hand function in individuals with extensive paralysis.
Journal of Neural Engineering 06/2011; 8(3):034003. · 3.28 Impact Factor
[show abstract][hide abstract] ABSTRACT: The ongoing pilot clinical trial of the BrainGate neural interface system aims in part to assess the feasibility of using neural activity obtained from a small-scale, chronically implanted, intracortical microelectrode array to provide control signals for a neural prosthesis system. Critical questions include how long implanted microelectrodes will record useful neural signals, how reliably those signals can be acquired and decoded, and how effectively they can be used to control various assistive technologies such as computers and robotic assistive devices, or to enable functional electrical stimulation of paralyzed muscles. Here we examined these questions by assessing neural cursor control and BrainGate system characteristics on five consecutive days 1000 days after implant of a 4 × 4 mm array of 100 microelectrodes in the motor cortex of a human with longstanding tetraplegia subsequent to a brainstem stroke. On each of five prospectively-selected days we performed time-amplitude sorting of neuronal spiking activity, trained a population-based Kalman velocity decoding filter combined with a linear discriminant click state classifier, and then assessed closed-loop point-and-click cursor control. The participant performed both an eight-target center-out task and a random target Fitts metric task which was adapted from a human-computer interaction ISO standard used to quantify performance of computer input devices. The neural interface system was further characterized by daily measurement of electrode impedances, unit waveforms and local field potentials. Across the five days, spiking signals were obtained from 41 of 96 electrodes and were successfully decoded to provide neural cursor point-and-click control with a mean task performance of 91.3% ± 0.1% (mean ± s.d.) correct target acquisition. Results across five consecutive days demonstrate that a neural interface system based on an intracortical microelectrode array can provide repeatable, accurate point-and-click control of a computer interface to an individual with tetraplegia 1000 days after implantation of this sensor.
Journal of Neural Engineering 03/2011; 8(2):025027. · 3.28 Impact Factor
[show abstract][hide abstract] ABSTRACT: Recent developments in neural interface systems hold the promise to restore movement in people with paralysis. In search of neural signals for control of neural interface systems, previous studies have investigated primarily single and multiunit activity, as well as low frequency local field potentials (LFPs). In this paper, we investigate the information content about grasping motion of a broad band high frequency LFP (200 Hz - 400 Hz) by classifying discrete grasp aperture states and decoding continuous aperture trajectories. LFPs were recorded via 96-microelectrode arrays in the primary motor cortex (M1) of two monkeys performing free 3-D reaching and grasping towards moving objects. Our results indicate that broad band high frequency LFPs could serve as useful signals for restoring a motor function such as grasp control.
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE; 10/2010
[show abstract][hide abstract] ABSTRACT: This study investigated the decoding of imagined arm movements from M1 in an individual with high level tetraplegia. The participant was instructed to imagine herself performing a series of single-joint arm movements, aided by the visual cue of an animate character performing these movements. System identification was used offline to predict the trajectories of the imagined movements and compare these predictions to the trajectories of the actual movements. We report rates of 25 - 50% for predicting completely imagined arm movements in the absence of a priori movements to aid in decoder building.
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE; 10/2010
[show abstract][hide abstract] ABSTRACT: Intracortical microelectrode array recordings generate a variety of neural signals with potential application as control signals in neural interface systems. Previous studies have focused on single and multiunit activity (MUA), as well as low-frequency local field potentials (LFPs), but have not explored higher frequency (>200 Hz) LFPs. In addition, the potential to decode 3-D reach and grasp kinematics based on LFPs has not been demonstrated. Here, we use mutual information and decoding analyses to probe the information content about 3-D reaching and grasping of seven different LFP frequency bands in the range of 0.3-400 Hz. LFPs were recorded via 96-microelectrode arrays in primary motor cortex (M1) of two monkeys performing free reaching to grasp moving objects. Mutual information analyses revealed that higher frequency bands (e.g., 100-200 and 200-400 Hz) carried the most information about the examined kinematics. Furthermore, Kalman filter decoding revealed that broad-band high frequency LFPs, likely reflecting MUA, provided the best decoding performance as well as substantial accuracy in reconstructing reach kinematics, grasp aperture, and aperture velocity. These results indicate that LFPs, especially high frequency bands, could be useful signals for neural interfaces controlling 3-D reach and grasp kinematics.
IEEE Transactions on Biomedical Engineering 08/2010; · 2.35 Impact Factor
[show abstract][hide abstract] ABSTRACT: We report on the performance of a wireless, implantable, neural recording platform. A multitude of neuroengineering challenges
exist today in creating practical, chronic multichannel neural recording systems for primate research and human clinical application.
Specifically, a) the persistent wired connections limit patient mobility from the recording system, b) the transfer of high
bandwidth signals to external (even distant) electronics normally forces premature data reduction, and c) the chronic susceptibility
to infection due to the percutaneous nature of the implants all severely hinder the success of neural prosthetic systems.
Here we detail a scalable 16-channel microsystem that can employ any of several modalities of power delivery (wire, radio
frequency induction, and a photovoltaic energy converter) and data transmission (wire, and transcutaneous infrared laser transmission).
Data is reported from a recent sub-chronic (~30 day) rhesus macaque MI implantation.
KeywordsNeural Interface-Brain Computer Interface-Neural Prosthetics
[show abstract][hide abstract] ABSTRACT: A multitude of neuroengineering challenges exist today in creating practical, chronic multichannel neural recording systems for primate research and human clinical application. Specifically, a) the persistent wired connections limit patient mobility from the recording system, b) the transfer of high bandwidth signals to external (even distant) electronics normally forces premature data reduction, and c) the chronic susceptibility to infection due to the percutaneous nature of the implants all severely hinder the success of neural prosthetic systems. Here we detail one approach to overcome these limitations: an entirely implantable, wirelessly communicating, integrated neural recording microsystem, dubbed the Brain Implantable Chip (BIC).
Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE; 10/2009
[show abstract][hide abstract] ABSTRACT: A direct neural interface system (NIS) promises to provide communication and independence to persons with paralysis by harnessing intact motor cortical signals to enable controlling prosthetic devices. An intracortical NIS aims to achieve this by sensing extracellular neuronal signals through chronically implanted microelectrodes and by decoding the spiking activity of neurons into prosthetic control signals. In non-human primate studies, decoding has been performed by finding a relationship between neuronal signals and actual limb movements. However, such decoding approaches face challenges in the case of paralyzed persons where there is no true movement information. Specifically, we have focused on dealing with several key questions in decoding of neural activity in humans with paralysis: what movement parameters should be decoded?; which decoding algorithms lead to more accurate estimation of movement parameters?; how do we train decoding algorithms without observing actual movement parameters?; and how many control parameters can be decoded from a single neural ensemble? In this paper, we summarize our recent studies to address these questions to improve decoding performance, which enables a human with tetraplegia to drive a 2D computer cursor to an arbitrary position and execute a ldquoclickrdquo on the area of interest.
Asian Control Conference, 2009. ASCC 2009. 7th; 09/2009
[show abstract][hide abstract] ABSTRACT: We have built a wireless implantable microelectronic device for transmitting cortical signals transcutaneously. The device is aimed at interfacing a cortical microelectrode array to an external computer for neural control applications. Our implantable microsystem enables 16-channel broadband neural recording in a nonhuman primate brain by converting these signals to a digital stream of infrared light pulses for transmission through the skin. The implantable unit employs a flexible polymer substrate onto which we have integrated ultra-low power amplification with analog multiplexing, an analog-to-digital converter, a low power digital controller chip, and infrared telemetry. The scalable 16-channel microsystem can employ any of several modalities of power supply, including radio frequency by induction, or infrared light via photovoltaic conversion. As of the time of this report, the implant has been tested as a subchronic unit in nonhuman primates (~ 1 month), yielding robust spike and broadband neural data on all available channels.
IEEE Transactions on Neural Systems and Rehabilitation Engineering 09/2009; · 3.26 Impact Factor
[show abstract][hide abstract] ABSTRACT: Activity-dependent synaptic plasticity is likely to provide a mechanism for learning and memory. Cortical synaptic responses that are strengthened within a fixed synaptic modification range after 5 days of motor skill learning are driven near the top of their range, leaving only limited room for additional synaptic strengthening. If synaptic strengthening is a requisite step for acquiring new skills, near saturation of long-term potentiation (LTP) should impede further learning or the LTP mechanism should recover after single-task learning. Here we show that the initial learning-induced synaptic enhancement is sustained even long after training has been discontinued and that the synaptic modification range shifts upward. This range shift places increased baseline synaptic efficacy back within the middle of its operating range, allowing prelearning levels of LTP and long-term depression. Persistent synaptic strengthening might be a substrate for long-term retention in motor cortex, whereas the shift in synaptic modification range ensures the availability for new synaptic strengthening.
Journal of Neurophysiology 01/2008; 98(6):3688-95. · 3.30 Impact Factor
[show abstract][hide abstract] ABSTRACT: We overview approaches to and current status of development of device technology for interfacing the brain via implantable microelectronic sensors for neuroengineering applications. A major direction aims to restore movement to disabled persons, whose healthy brain is envisioned to send direct commands e.g. to real or artificial limbs via application specific electronic communication links.
[show abstract][hide abstract] ABSTRACT: Basic neural prosthetic control of a computer cursor has been recently demonstrated by Hochberg et al. (2006) using the BrainGate system (Cyberkinetics Neurotechnology Systems, Inc.). While these results demonstrate the feasibility of intracortically-driven prostheses for humans with paralysis, a practical cursor-based computer interface requires more precise cursor control and the ability to "click" on areas of interest. Here we present the first practical point and click device that decodes both continuous states (e.g. cursor kinematics) and discrete states (e.g. click states) from a single neural population in human motor cortex. We describe a probabilistic multi-state decoder and the necessary training paradigms that enable point and click cursor control by a human with tetraplegia using an implanted microelectrode array. We present results from multiple recording sessions and quantify the point and click performance
[show abstract][hide abstract] ABSTRACT: The direct neural control of external prosthetic devices such as robot hands requires the accurate decoding of neural activity representing continuous movement. This requirement becomes formidable when multiple degrees of freedom (DoFs) are to be controlled as in the case of the fingers of a robotic hand. In this paper a methodology is proposed for estimating grasp aperture using the spiking activity of multiple neurons recorded with an electrode array implanted in the arm/hand area of primary motor cortex (Ml). Grasp aperture provides a reasonable approximation to the hand configuration during grasping tasks, while it offers a large reduction in the number of DoFs that must be estimated. A family of state space models with hidden variables is used to decode each finger grasp aperture with respect to the thumb from a population of motor-cortical neurons. The firing rates of multiple neurons in Ml were found to be correlated with grasp aperture and were used as inputs to our decoding algorithm. The proposed decoding architecture was evaluated off-line by decoding pre-recorded neural activity from monkey motor cortex during a natural grasping task. We found that our model was able to accurately reconstruct finger grasp aperture from a small population of cells. This demonstrates the first decoding of continuous grasp aperture from Ml suggesting the feasibility for neural control of prosthetic robotic hands from neuronal population signals
[show abstract][hide abstract] ABSTRACT: Brain-computer interfaces (BCIs) hold the promise to restore mobility and independence to persons with paralysis. In spinal cord injury, brainstem stroke, and a host of neuromuscular disorders, the intact brain is "disconnected" from its intact target (such as a limb or the facial musculature), preventing mobility and - in locked-in syndrome and severe amyotrophic lateral sclerosis (ALS) - precluding even meaningful verbal communication. If it becomes possible to discern the movement intention of someone with paralysis - reliably, safely, and in real time - it would then be possible to provide not only a robust new method of communication but eventually the ability to gain control over a prosthetic limb or, by connecting to additional technologies, one's own limbs. In this review, we survey several methods for revealing neural activity in the human brain and their potential for re-enabling mobility in persons with severe paralysis
IEEE Engineering in Medicine and Biology Magazine 10/2006; · 26.30 Impact Factor
[show abstract][hide abstract] ABSTRACT: This paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop charged with reviewing and evaluating the current state of and issues relevant to brain-computer interface (BCI) clinical applications. These include potential BCI users, applications, validation, getting BCIs to users, role of government and industry, plasticity, and ethics.
IEEE Transactions on Neural Systems and Rehabilitation Engineering 07/2006; 14(2):131-4. · 3.26 Impact Factor
[show abstract][hide abstract] ABSTRACT: Neural prosthetic technology has moved from the laboratory to clinical settings with human trials. The motor cortical control of devices in such settings raises important questions about the design of computational interfaces that produce stable and reliable control over a wide range of operating conditions. In particular, non-stationarity of the neural code across different behavioral conditions or attentional states becomes a potential issue. Non-stationarity has been previously observed in animals where the encoding model representing the mathematical relationship between neural population activity and behavioral variables such as hand motion changes over time. If such an encoding model is formed and learned during a particular training period, decoding performance (neural control) with the model may not be consistent during successive periods even when the same task is repeated. It is critical in both laboratory experiments and in clinical settings to be able to evaluate whether the representation of movement encoded by a neural population has changed or not. Such information can be used as a cue to retrain the system or as feedback to an adaptive decoding algorithm. To that end, we develop a statistical methodology to evaluate changes in the neural code over time using a generative probabilistic decoding model. The changes are evaluated by comparing the likelihoods of firing rates given similar distributions of 2D hand kinematics collected while a primate periodically performs a manual cursor control task. A comparison is performed by measuring a distance between probabilistic encoding models trained at different times. The statistical significance of the distance measurements are justified with a systematic statistical hypothesis test. The experimental results demonstrate that the likelihood changes over different periods with the change being greater when more distant periods are compared
Biomedical Robotics and Biomechatronics, 2006. BioRob 2006. The First IEEE/RAS-EMBS International Conference on; 02/2006
[show abstract][hide abstract] ABSTRACT: A number of studies of the motor system suggest that the majority of primary motor cortical neurons represent simple movement-related kinematic and dynamic quantities in their time-varying activity patterns. An example of such an encoding relationship is the cosine tuning of firing rate with respect to the direction of hand motion. We present a systematic development of statistical encoding models for movement-related motor neurons using multielectrode array recordings during a two-dimensional (2-D) continuous pursuit-tracking task. Our approach avoids massive averaging of responses by utilizing 2-D normalized occupancy plots, cascaded linear-nonlinear (LN) system models and a method for describing variability in discrete random systems. We found that the expected firing rate of most movement-related motor neurons is related to the kinematic values by a linear transformation, with a significant nonlinear distortion in about 1/3 of the neurons. The measured variability of the neural responses is markedly non-Poisson in many neurons and is well captured by a "normalized-Gaussian" statistical model that is defined and introduced here. The statistical model is seamlessly integrated into a nearly-optimal recursive method for decoding movement from neural responses based on a Sequential Monte Carlo filter.
IEEE Transactions on Biomedical Engineering 08/2005; · 2.35 Impact Factor
[show abstract][hide abstract] ABSTRACT: Over the past ten years, we have tested and helped develop a multi-electrode array for chronic cortical recordings in behaving non-human primates. We have found that it is feasible to record from dozens of single units in the motor cortex for extended periods of time and that these signals can be decoded in a closed-loop, real-time system to generate goal-directed behavior of external devices. This work has culminated in a FDA clinical trial that has demonstrated that a tetraplegic patient can voluntarily modulate motor cortical activity in order to move a computer cursor to visual targets. Further advances in BMI technology using non-human primates have focused on using multiple modes of control from signals in different cortical areas. We demonstrate that primary motor cortical activity may be optimized for continuous movement control whereas signals from the premotor cortex may be better suited for discrete target selection. We propose a hybrid BMI whereby decoding can be voluntarily switched from discrete to continuous control modes
Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the; 02/2005
[show abstract][hide abstract] ABSTRACT: Recent methods for motor cortical decoding have demonstrated relatively accurate reconstructions of hand trajectory from small populations of neurons in primary motor cortex. Decoding results are often reported only for periods when the subject is attending to the task. In a neural prosthetic interface, however, the subject must be able to switch between controlling a device or performing other mental functions. In this work we demonstrate a method for detecting whether or not a subject is attending to a motor control task. Using the firing activity of the same neural population used for decoding hand kinematics we demonstrate that a Fisher linear discriminant performs well in classifying the attentional state of a monkey. We use the output of this classifier to augment a hidden state in a first order Markov model and use particle filtering to recursively infer hand kinematics and attentional state conditioned on neural firing rates. We demonstrate high accuracy on test data where a monkey switches between attending to a task and not. By decoding a discrete "state" in addition to hand kinematics our proposed classification and estimation scheme may enable real-world neuroprosthetic functions such as "hold", "click", and "turn off/on"
Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the; 02/2005
[show abstract][hide abstract] ABSTRACT: Extracellular recordings of motor cortex (MI) neurons, using a chronically implanted multi-electrode array, promise to yield a high dimensional input signal to external devices such as a computer, exoskeleton or prosthetic arm. For the multi-electrode array to be used as a sensor for a neuromotor prosthesis (NMP), it is important that it continually record movement-related signals over long time periods. Recent studies have demonstrated that it is possible to continually record for up to 1.5 years from a sufficient number of MI neurons in monkeys to enable neural decoding of arm movement. Cyberkinetics Neurotechnology Systems Inc. has initiated an investigational device exemption (TOE) study investigating the safety and efficacy of the BrainGate™ Neural Interface System, a medical device that combines this sensor with data acquisition and processing devices to decode movement intent. This device is currently being investigated as a means for a quadriplegic person to operate a range of assistive technologies. Preliminary results from this case study provide evidence that (1) MI neurons remain active more than 3 years after spinal cord injury, (2) units can be recorded 6 months after surgery. This technology may benefit quadriplegic people by providing a new output pathway from the cortex, to control their muscles.
Rehabilitation Robotics, 2005. ICORR 2005. 9th International Conference on; 01/2005