Toward the Restoration of Hand Use to a Paralyzed Monkey: Brain-Controlled Functional Electrical Stimulation of Forearm Muscles

Loss of hand use is considered by many spinal cord injury survivors to be the most devastating consequence of their injury. Functional electrical stimulation (FES) of forearm and hand muscles has been used to provide basic, voluntary hand grasp to hundreds of human patients. Current approaches typically grade pre-programmed patterns of muscle activation using simple control signals, such as those derived from residual movement or muscle activity. However, the use of such fixed stimulation patterns limits hand function to the few tasks programmed into the controller. In contrast, we are developing a system that uses neural signals recorded from a multi-electrode array implanted in the motor cortex; this system has the potential to provide independent control of multiple muscles over a broad range of functional tasks. Two monkeys were able to use this cortically controlled FES system to control the contraction of four forearm muscles despite temporary limb paralysis. The amount of wrist force the monkeys were able to produce in a one-dimensional force tracking task was significantly increased. Furthermore, the monkeys were able to control the magnitude and time course of the force with sufficient accuracy to track visually displayed force targets at speeds reduced by only one-third to one-half of normal. Although these results were achieved by controlling only four muscles, there is no fundamental reason why the same methods could not be scaled up to control a larger number of muscles. We believe these results provide an important proof of concept that brain-controlled FES prostheses could ultimately be of great benefit to paralyzed patients with injuries in the mid-cervical spinal cord

Modeling and Decoding Motor Cortical Activity Using a Switching Kalman Filter

We present a switching Kalman filter model for the real-time inference of hand kinematics from a population of motor cortical neurons. Firing rates are modeled as a Gaussian mixture where the mean of each Gaussian component is a linear function of hand kinematics. A "hidden state" models the probability of each mixture component and evolves over time in a Markov chain. The model generalizes previous encoding and decoding methods, addresses the non-Gaussian nature of firing rates, and can cope with crudely sorted neural data common in on-line prosthetic applications.Mathematic

Central nervous system microstimulation: Towards selective micro-neuromodulation

Electrical stimulation technologies capable of modulating neural activity are well established for neuroscientific research and neurotherapeutics. Recent micro-neuromodulation experimental results continue to explain neural processing complexity and suggest the potential for assistive technologies capable of restoring or repairing of basic function. Nonetheless, performance is dependent upon the specificity of the stimulation. Increasingly specific stimulation is hypothesized to be achieved by progressively smaller interfaces. Miniaturization is a current focus of neural implants due to improvements in mitigation of the body's foreign body response. It is likely that these exciting technologies will offer the promise to provide large-scale micro-neuromodulation in the future. Here, we highlight recent successes of assistive technologies through bidirectional neuroprostheses currently being used to repair or restore basic brain functionality. Furthermore, we introduce recent neuromodulation technologies that might improve the effectiveness of these neuroprosthetic interfaces by increasing their chronic stability and microstimulation specificity. We suggest a vision where the natural progression of innovative technologies and scientific knowledge enables the ability to selectively micro-neuromodulate every neuron in the brain

Cortical Decoding of Individual Finger Group Motions Using ReFIT Kalman Filter

Objective: To date, many brain-machine interface (BMI) studies have developed decoding algorithms for neuroprostheses that provide users with precise control of upper arm reaches with some limited grasping capabilities. However, comparatively few have focused on quantifying the performance of precise finger control. Here we expand upon this work by investigating online control of individual finger groups.Approach: We have developed a novel training manipulandum for non-human primate (NHP) studies to isolate the movements of two specific finger groups: index and middle-ring-pinkie (MRP) fingers. We use this device in combination with the ReFIT (Recalibrated Feedback Intention-Trained) Kalman filter to decode the position of each finger group during a single degree of freedom task in two rhesus macaques with Utah arrays in motor cortex. The ReFIT Kalman filter uses a two-stage training approach that improves online control of upper arm tasks with substantial reductions in orbiting time, thus making it a logical first choice for precise finger control.Results: Both animals were able to reliably acquire fingertip targets with both index and MRP fingers, which they did in blocks of finger group specific trials. Decoding from motor signals online, the ReFIT Kalman filter reliably outperformed the standard Kalman filter, measured by bit rate, across all tested finger groups and movements by 31.0 and 35.2%. These decoders were robust when the manipulandum was removed during online control. While index finger movements and middle-ring-pinkie finger movements could be differentiated from each other with 81.7% accuracy across both subjects, the linear Kalman filter was not sufficient for decoding both finger groups together due to significant unwanted movement in the stationary finger, potentially due to co-contraction.Significance: To our knowledge, this is the first systematic and biomimetic separation of digits for continuous online decoding in a NHP as well as the first demonstration of the ReFIT Kalman filter improving the performance of precise finger decoding. These results suggest that novel nonlinear approaches, apparently not necessary for center out reaches or gross hand motions, may be necessary to achieve independent and precise control of individual fingers

Development of a Unique Whole-Brain Model for Upper Extremity Neuroprosthetic Control

Neuroprostheses are at the forefront of upper extremity function restoration. However, contemporary controllers of these neuroprostheses do not adequately address the natural brain strategies related to planning, execution and mediation of upper extremity movements. These lead to restrictions in providing complete and lasting restoration of function. This dissertation develops a novel whole-brain model of neuronal activation with the goal of providing a robust platform for an improved upper extremity neuroprosthetic controller. Experiments (N=36 total) used goal-oriented upper extremity movements with real-world objects in an MRI scanner while measuring brain activation during functional magnetic resonance imaging (fMRI). The resulting data was used to understand neuromotor strategies using brain anatomical and temporal activation patterns. The study\u27s fMRI paradigm is unique and the use of goal-oriented movements and real-world objects are crucial to providing accurate information about motor task strategy and cortical representation of reaching and grasping. Results are used to develop a novel whole-brain model using a machine learning algorithm. When tested on human subject data, it was determined that the model was able to accurately distinguish functional motor tasks with no prior knowledge. The proof of concept model created in this work should lead to improved prostheses for the treatment of chronic upper extremity physical dysfunction

Toward a Full Prehension Decoding from Dorsomedial Area V6A

Neural prosthetics represent a promising approach to restore movements in patients affected by spinal cord lesions. To drive a full capable, brain controlled, prosthetic arm, reaching and grasping components of prehension have to be accurately reconstructed from neural activity. Neurons in the dorsomedial area V6A of macaque show sensitivity to reaching direction accounting also for depth dimension, thus encoding positions in the entire 3D space. Moreover, many neurons are sensible to grips types and wrist orientations. To assess whether these signals are adequate to drive a full capable neural prosthetic arm, we recorded spiking activity of neurons in area V6A, spike counts were used to train machine learning algorithms to reconstruct reaching and grasping. In a first work, two Macaca fascicularis monkeys were trained to perform an instructed-delay reach-to-grasp task in the dark and in the light toward objects of different shapes. The activity of 89 neurons was used to train and validate a Bayes classifier used for decoding objects and grip types. Recognition rates were well above chance level for all the epochs analyzed in this study. In a second work, monkeys were trained to perform reaches to targets located at various depths and directions and the classifier was tested whether it could correctly predict the reach goal position from V6A signals. The reach goal location was reliably decoded with accuracy close to optimal (>90%) throughout the task. Together these results, show a reliable decoding of hand grips and spatial location of reaching goals in the same area, suggesting that V6A is a suitable site to decode the entire prehension action with obvious advantages in terms of implant invasiveness. This new PPC site useful for decoding both reaching and grasping opens new perspectives in the development of human brain-computer interfaces

Implantable Neural Interfaces for Direct Control of Hand Prostheses

State-of-the art robotic hands can mimic many functions of the human hand. These devices are capable of actuating individual finger and multi-joint movements while providing adequate gripping force for daily activities. However, for patients with spinal cord injuries or amputations, there are few options to control these functions seamlessly or intuitively. A common barrier to restoring hand function to both populations is a lack of high-fidelity control signals. Non-invasive electrophysiological techniques record global summations of activity and lack the spatial or temporal resolution to extract or “decode” precise movement commands. The ability to decode finger movements from the motor system would allow patients to directly control hand functions and provide intuitive and scalable prosthetic solutions. This thesis investigates the capabilities of implantable devices to provide finger-specific commands for prosthetic hands. We adapt existing reasoning algorithms to two different sensing technologies. The first is intracortical electrode arrays implanted into primary motor cortex of two non-human primates. Both subjects controlled a virtual hand with a regression algorithm that decoded brain activity into finger kinematics. Performance was evaluated with single degree of freedom target matching tasks. Bit rate is a throughput metric that accounts for task difficulty and movement precision. A state-of-the-art re-calibration approach improved throughputs by an average of 33.1%. Notably, decoding performance was not dependent on subjects moving their intact hands. In future research, this approach can improve grasp precision for patients with spinal cord injuries. The second sensing technology is intramuscular electrodes implanted into residual muscles and Regenerative Peripheral Nerve Interfaces of two patients with transradial amputations. Both participants used a high-speed pattern recognition system to switch between 10 individual finger and wrist postures in a virtual environment with an average completion rate of 96.3% and a movement delay of 0.26 seconds. When the set was reduced to five grasp postures, average metrics improved to 100% completion and a 0.14 second delay. These results are a significant improvement over previous studies which report average completion rates ranging from 53.9% to 86.9% and delays of 0.45 to 0.86 seconds. Furthermore, grasp performance remained reliable across arm positions and both participants used this controller to complete a functional assessment with robotic prostheses. For a more dexterous solution, we combined the high-speed pattern recognition system with a regression algorithm that enabled simultaneous position control of both the index finger and middle-ring-small finger group. Both patients used this system to complete a virtual two degree of freedom target matching task with throughputs of 1.79 and 1.15 bits per second each. The controllers in this study used only four and five differentiated inputs, which can likely be processed with portable or implantable hardware. These results demonstrate that implantable sensors can provide patients with fluid and precise control of hand prostheses. However, clinically translatable implantable electronics need to be developed to realize the potential of these sensing and reasoning approaches. Further advancement of this technology will likely increase the utility and demand of robotic prostheses.PHDRoboticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169798/1/akvaskov_1.pd

Brain Waves, A Cultural History: Oscillations of Neuroscience, Technology, Telepathy, and Transcendence

This project proceeds from a narrow question: What, if anything, is a brain wave? Beguiling in its simplicity, this question prompts a cultural-historical investigation that spans over 150 years of science, technology, and society. Proposed in 1869, the original theory of brain waves cites etheric undulations to explain reports of apparent thought transference. Though most modern thinkers no longer believe in outright telepathy, I argue that dreams of thought transmission and other mental miracles subtly persist—not in obscure and occult circles, but at the forefront of technoscience. A hybrid of science and fiction, brain waves represent an ideal subject through which to explore the ways in which technical language shrouds spiritual dreams. Today, the phrase “brain waves” often function as shorthand for electrical changes in the brain, particularly in the context of technologies that purport to “read” some aspect of mental function, or to transmit neural data to a digital device. While such technologies appear uniquely modern, the history of brain waves reveals that they are merely the millennial incarnation of a much older hope—a hope for transmission and transcendence via the brain’s emanations