Brain-computer interface (BCI) research has progressed rapidly, with BCIs shifting from animal tests to human demonstrations of controlling computer cursors and even advanced prosthetic limbs, the latter having been the goal of the Revolutionizing Prosthetics (RP) program. These achievements now include direct electrical intracortical microstimulation (ICMS) of the brain to provide human BCI users feedback information from the sensors of prosthetic limbs. These successes raise the question of how well people would be able to use BCIs to interact with systems that are not based directly on the body (e.g., prosthetic arms), and how well BCI users could interpret ICMS information from such devices. If paralyzed individuals could use BCIs to effectively interact with such non-anthropomorphic systems, it would offer them numerous new opportunities to control novel assistive devices. Here we explore how well a participant with tetraplegia can detect infrared (IR) sources in the environment using a prosthetic arm mounted camera that encodes IR information via ICMS. We also investigate how well a BCI user could transition from controlling a BCI based on prosthetic arm movements to controlling a flight simulator, a system with different physical dynamics than the arm. In that test, the BCI participant used environmental information encoded via ICMS to identify which of several upcoming flight routes was the best option. For both tasks, the BCI user was able to quickly learn how to interpret the ICMSprovided information to achieve the task goals.

Retinal prosthetic devices can significantly and positively impact the ability of visually challenged individuals to live a more independent life. We describe a visual processing system which leverages image analysis techniques to produce visual patterns and allows the user to more effectively perceive their environment. These patterns are used to stimulate a retinal prosthesis to allow self guidance and a higher degree of autonomy for the affected individual. Specifically, we describe an image processing pipeline that allows for object and face localization in cluttered environments as well as various contrast enhancement strategies in the "implanted image." Finally, we describe a real-time implementation and deployment of this system on the Argus II platform. We believe that these advances can significantly improve the effectiveness of the next generation of retinal prostheses.

Spatial mapping, the location in space of a perceived location due to an implanted electrode's electrical stimulation is important in the design of visual prostheses. Generally, a visual prosthesis system consists of an implanted electrode array, an external camera that acquires the image, and a transmitter that sends the information to the implanted electrodes. In cortical visual implant, the layout of the implanted array in most cases does not match the retinotopic map and it is necessary to find the location of the percept of each electrode in world coordinates. Herein, we show the feasibility of using eye movements as markers to construct the spatial map of the implanted electrodes. A blind patient implanted with the Argus II retinal prosthesis was instructed to conduct an eye movement to the location of a percept generated by an electrical stimulation at different retinal locations. By analyzing the eye movements triggered by the electrical stimulation, we were able to reconstruct the spatial map of the electrodes. Our experiment demonstrates that a blind person still maintains control of eye movements that can be used to map the percept location of the implanted electrodes.

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 subject's 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.

In early 2006, APL was awarded a contract to start the first phase of the Revolutionizing Prosthetics 2009 program, a multiyear, multimillion-dollar effort to develop an advanced upper-extremity prosthetic limb, the Modular Prosthetic Limb (MPL). This program's goal is to develop an advanced limb that would allow a prosthetic wearer to button a shirt, tune a radio, and feel the warmth of a loved one's hand; such a limb might even provide the warfighter with the opportunity to return to active duty. Currently in the third phase of Revolutionizing Prosthetics, APL is leading the Defense Advanced Research Projects Agency's effort to provide upper-extremity functionality to people who have no ability to control their native arms, such as amputees and those with high spinal cord injury or neurodegenerative diseases such as amyotrophic lateral sclerosis. Phase 3 of the program involves significant focus on clinical activities including maturing the MPL's robustness, submitting regulatory filings, designing and executing preclinical and clinical activities, and advancing cortical implant technology. Initial clinical activities during this third year of Phase 3 have involved a subject with tetraplegia using a brain computer interface to control the MPL. Further trials with this subject continue as the program team prepares for four additional subjects during Phase 3.

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.