Kapil D. Katyal

Behavioral Demonstration of a Somatosensory Neuroprosthesis

Tactile sensation is critical for effective object manipulation, but current prosthetic upper limbs make no provision for delivering somesthetic feedback to the user. For individuals who require use of prosthetic limbs, this lack of feedback transforms a mundane task into one that requires extreme concentration and effort. Although vibrotactile motors and sensory substitution devices can be used to convey gross sensations, a direct neural interface is required to provide detailed and intuitive sensory feedback. In light of this, we describe the implementation of a somatosensory prosthesis with which we elicit, through intracortical microstimulation (ICMS), percepts whose magnitude is graded according to the force exerted on the prosthetic finger. Specifically, the prosthesis consists of a sensorized finger, the force output of which is converted into a regime of ICMS delivered to primary somatosensory cortex through chronically implanted multi-electrode arrays. We show that the performance of animals (Rhesus macaques) on a tactile task is equivalent whether stimuli are delivered to the native finger or to the prosthetic finger.

Demonstration of a Semi-Autonomous Hybrid Brain–Machine Interface Using Human Intracranial EEG, Eye Tracking, and Computer Vision to Control a Robotic Upper Limb Prosthetic

To increase the ability of brain-machine interfaces (BMIs) to control advanced prostheses such as the modular prosthetic limb (MPL), we are developing a novel system: the Hybrid Augmented Reality Multimodal Operation Neural Integration Environment (HARMONIE). This system utilizes hybrid input, supervisory control, and intelligent robotics to allow users to identify an object (via eye tracking and computer vision) and initiate (via brain-control) a semi-autonomous reach-grasp-and-drop of the object by the MPL. Sequential iterations of HARMONIE were tested in two pilot subjects implanted with electrocortico-graphic (ECoG) and depth electrodes within motor areas. The subjects performed the complex task in 71.4% (20/28) and 67.7% (21/31) of trials after minimal training. Balanced accuracy for detecting movements was 91.1% and 92.9%, significantly greater than chance accuracies (p <; 0.05). After BMI-based initiation, the MPL completed the entire task 100% (one object) and 70% (three objects) of the time. The MPL took approximately 12.2 s for task completion after system improvements implemented for the second subject. Our hybrid-BMI design prevented all but one baseline false positive from initiating the system. The novel approach demonstrated in this proof-of-principle study, using hybrid input, supervisory control, and intelligent robotics, addresses limitations of current BMIs.

Individual finger control of a modular prosthetic limb using high-density electrocorticography in a human subject

OBJECTIVE We used native sensorimotor representations of fingers in a brain-machine interface (BMI) to achieve immediate online control of individual prosthetic fingers. APPROACH Using high gamma responses recorded with a high-density electrocorticography (ECoG) array, we rapidly mapped the functional anatomy of cued finger movements. We used these cortical maps to select ECoG electrodes for a hierarchical linear discriminant analysis classification scheme to predict: (1) if any finger was moving, and, if so, (2) which digit was moving. To account for sensory feedback, we also mapped the spatiotemporal activation elicited by vibrotactile stimulation. Finally, we used this prediction framework to provide immediate online control over individual fingers of the Johns Hopkins University Applied Physics Laboratory modular prosthetic limb. MAIN RESULTS The balanced classification accuracy for detection of movements during the online control session was 92% (chance: 50%). At the onset of movement, finger classification was 76% (chance: 20%), and 88% (chance: 25%) if the pinky and ring finger movements were coupled. Balanced accuracy of fully flexing the cued finger was 64%, and 77% had we combined pinky and ring commands. Offline decoding yielded a peak finger decoding accuracy of 96.5% (chance: 20%) when using an optimized selection of electrodes. Offline analysis demonstrated significant finger-specific activations throughout sensorimotor cortex. Activations either prior to movement onset or during sensory feedback led to discriminable finger control. SIGNIFICANCE Our results demonstrate the ability of ECoG-based BMIs to leverage the native functional anatomy of sensorimotor cortical populations to immediately control individual finger movements in real time.

Simultaneous Neural Control of Simple Reaching and Grasping With the Modular Prosthetic Limb Using Intracranial EEG

Intracranial electroencephalographic (iEEG) signals from two human subjects were used to achieve simultaneous neural control of reaching and grasping movements with the Johns Hopkins University Applied Physics Lab (JHU/APL) Modular Prosthetic Limb (MPL), a dexterous robotic prosthetic arm. We performed functional mapping of high gamma activity while the subject made reaching and grasping movements to identify task-selective electrodes. Independent, online control of reaching and grasping was then achieved using high gamma activity from a small subset of electrodes with a model trained on short blocks of reaching and grasping with no further adaptation. Classification accuracy did not decline (p <; 0.05, one-way ANOVA) over three blocks of testing in either subject. Mean classification accuracy during independently executed overt reach and grasp movements for (Subject 1, Subject 2) were (0.85, 0.81) and (0.80, 0.96), respectively, and during simultaneous execution they were (0.83, 0.88) and (0.58, 0.88), respectively. Our models leveraged knowledge of the subjects individual functional neuroanatomy for reaching and grasping movements, allowing rapid acquisition of control in a time-sensitive clinical setting. We demonstrate the potential feasibility of verifying functionally meaningful iEEG-based control of the MPL prior to chronic implantation, during which additional capabilities of the MPL might be exploited with further training.

Collaborative approach in the development of high-performance brain-computer interfaces for a neuroprosthetic arm: Translation from animal models to human control

Our research group recently demonstrated that a person with tetraplegia could use a brain–computer interface (BCI) to control a sophisticated anthropomorphic robotic arm with skill and speed approaching that of an able‐bodied person. This multiyear study exemplifies important principles in translating research from foundational theory and animal experiments into a clinical study. We present a roadmap that may serve as an example for other areas of clinical device research as well as an update on study results. Prior to conducting a multiyear clinical trial, years of animal research preceded BCI testing in an epilepsy monitoring unit, and then in a short‐term (28 days) clinical investigation. Scientists and engineers developed the necessary robotic and surgical hardware, software environment, data analysis techniques, and training paradigms. Coordination among researchers, funding institutes, and regulatory bodies ensured that the study would provide valuable scientific information in a safe environment for the study participant. Finally, clinicians from neurosurgery, anesthesiology, physiatry, psychology, and occupational therapy all worked in a multidisciplinary team along with the other researchers to conduct a multiyear BCI clinical study. This teamwork and coordination can be used as a model for others attempting to translate basic science into real‐world clinical situations.

Revolutionizing Prosthetics software technology

The Defense Advanced Research Projects Agency Revolutionizing Prosthetics 2009 program has the goal of developing a neurally-integrated upper limb prosthesis to provide amputee soldiers with pre-injury levels of function. A team of over 30 collaborating institutions from industry and academia set out to design and construct a prosthetic limb that achieves natural human limb performance by combining the state of the art in neuroscience, robotics, sensors, power systems, actuation, and complex embedded software. The Modular Prosthetic Limb (MPL) is the result of Phase II of the program which successfully demonstrated robotic limb operation in December 2009. MPL design and development addressed several areas of technical complexity such as space, power, and mass constraints, safety-critical operation, and modularity for patient customizations based on the location of disarticulation. This paper provides an overview of the MPL system architecture, the robotic arm control strategy, and detailed information on the embedded system software that integrates the MPL components to enable patient-based control.

A collaborative BCI approach to autonomous control of a prosthetic limb system

Existing brain-computer interface (BCI) control of highly dexterous robotic manipulators and prosthetic devices typically rely solely on neural decode algorithms to determine the users intended motion. Although these approaches have made significant progress in the ability to control high degree of freedom (DOF) manipulators, the ability to perform activities of daily living (ADL) is still an ongoing research endeavor. In this paper, we describe a hybrid system that combines elements of autonomous robotic manipulation with neural decode algorithms to maneuver a highly dexterous robotic manipulator for a reach and grasp task. This system was demonstrated using a human patient with cortical micro-electrode arrays allowing the user to manipulate an object on a table and place it at a desired location. The preliminary results for this system are promising in that it demonstrates the potential to blend robotic control to perform lower level manipulation tasks with neural control that allows the user to focus on higher level tasks thereby reducing the cognitive load and increasing the success rate of performing ADL type activities.

Approaches to robotic teleoperation in a disaster scenario: From supervised autonomy to direct control

The ability of robotic systems to effectively address disaster scenarios that are potentially dangerous for human operators is continuing to grow as a research and development field. This leverages research from areas such as bimanual manipulation, dexterous grasping, bipedal locomotion, computer vision, sensing, object segmentation, varying degrees of autonomy, and operator control/feedback. This paper describes the development of a semi-autonomous bimanual dexterous robotic system that comes to the aid of a mannequin simulating an injured victim by operating a fire extinguisher, affixing a cervical collar, cooperatively placing the victim on a spineboard with another bimanual robot, and relocating the victim. This system accomplishes these tasks through a series of control modalities that range from supervised autonomy to full teleoperation and allows the control model to be chosen and optimized for a specific subtask. We present a description of the hardware platform, the software control architecture, a human-in-the-loop computer vision algorithm, and an infrastructure to use a variety of user input devices in combination with autonomous control to compete several dexterous tasks. The effectiveness of the system was demonstrated in both laboratory and live outdoor demonstrations.

HARMONIE: A multimodal control framework for human assistive robotics

Effective user control of highly dexterous and robotic assistive devices requires intuitive and natural modalities. Although surgically implanted brain-computer interfaces (BCIs) strive to achieve this, a number of non-invasive engineering solutions may provide a quicker path to patient use by eliminating surgical implantation. We present the development of a semi-autonomous control system that utilizes computer vision, prosthesis feedback, effector centric device control, smooth movement trajectories, and appropriate hand conformations to interact with objects of interest. Users can direct a prosthetic limb through an intuitive graphical user interface to complete multi-stage tasks using patient appropriate combinations of control inputs such as eye tracking, conventional prosthetic controls/joysticks, surface electromyography (sEMG) signals, and neural interfaces (ECoG, EEG). Aligned with activities of daily living (ADL), these tasks include directing the prosthetic to specific locations or objects, grasping of objects by modulating hand conformation, and action upon grasped objects such as self-feeding. This Hybrid Augmented Reality Multimodal Operation Neural Integration Environment (HARMONIE) semi-autonomous control system lowers the users cognitive load, leaving the bulk of command and control of the device to the computer. This flexible and intuitive control system could serve patient populations ranging from wheelchair-bound quadriplegics to upper-limb amputees.

Leveraging Deep Reinforcement Learning for Reaching Robotic Tasks

This work leverages Deep Reinforcement Learning (DRL) to make robotic control immune to changes in the robot manipulator or the environment and to perform reaching, collision avoidance and grasping without explicit, prior and fine knowledge of the human arm structure and kinematics, without careful hand-eye calibration, solely based on visual/retinal input, and in ways that are robust to environmental changes. We learn a manipulation policy which we show takes the first steps toward generalizing to changes in the environment and can scale and adapt to new manipulators. Experiments are aimed at a) comparing different DCNN network architectures b) assessing the reward prediction for two radically different manipulators and c) performing a sensitivity analysis comparing a classical visual servoing formulation of the reaching task with the proposed DRL method.