Mobile Signal Processing System for Broadband Neural Decoding

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DESCRIPTION (provided by applicant): Developing technologies to assist persons with severe movement disabilities, paralysis, locked-in syndrome or limb amputations is a priority for the Department of Veterans Affairs. ALS (Lou Gehrig's disease), which results in complete paralysis, has a disproportionately high incidence among the veteran population. Due to injuries sustained in recent conflicts, care for veterans with upper extremity amputation is increasing. The VA has advanced assistive technologies including direct neural control of communication software for persons with ALS, revolutionary prosthetic arms, and functional electrical stimulation (FES) of paralyzed limbs. Evidence from the ongoing pilot clinical trial of BrainGate2 (IDE), an implantable brain signal sensor coupled with a neural decoding system, indicates that individuals with paralysis can use their brain signals to control software or robotic/prosthetic arms years after spinal cord injury, brain stem stroke, or the onset of ALS. Although direct brain control promises effective, adept control of enabling technologies, current neural decoding algorithms run on cumbersome, immobile computer systems. Reducing these platforms to a compact, wearable device would achieve cornerstone advances that could enable persons with paralysis to move themselves in a wheelchair independently under brain control, allow persons with locked- in syndrome to access neurally-controlled communication tools anywhere, permit ambulatory individuals with limb amputation to control advanced prostheses such as the DEKA arm/hand with their own thoughts, or let people with upper limb paralysis reach and grasp with their own muscles using brain-controlled FES. The proposed research achieves this goal by exploiting commercially-available mobile microsystem technology to produce a battery-powered, high-performance wearable device capable of wirelessly receiving, processing, and decoding brain signals and generating commands to operate nearby mobile assistive technologies. First, a powerful new generation of programmable gate arrays (FPGAs) allows neural decoding algorithm software that currently runs on a Windows computer to be converted to a hardware description language (HDL) that runs orders of magnitude faster on a fingernail-sized FPGA chip. For research, FPGAs can readily be re-programmed to test novel neural decoding algorithms. Second, high-performance, low- power processors developed for mobile applications provide the requisite data management and wireless communication capabilities to receive neural signals and transmit commands. The integrated system will execute all signal processing and decoding functions required by the present BrainGate2 neural interface system yet in a wearable, battery powered package. Interface software will be developed to allow engineers to rapidly reconfigure the device and to allow individuals with disability (or their assistants) to adjust the device durig use. Functionality will be validated in the BrainGate simulation environment. Then, individuals with tetraplegia in the BrainGate2 trial will test it while controlling assistive software and prosthetic devices. Research will be performed at the Providence VA Medical Center and Brown University. This research team has well-establish expertise in the development of innovative microelectronics and neural prosthetic systems. The Principle Investigator has directed BrainGate systems engineering and development for years with productivity both in neural prosthetics research and in industry developing innovative microelectronic systems. The PI and co-investigators have demonstrated the consistent ability to integrate engineering, neuroscience, computer science, and clinical expertise to deliver meaningful leading-edge research that is transforming neural prosthetic technology to assist persons with severe movement disability. By integrating this expertise, the current proposal will yield a wearable device with the unprecedented data throughput and processing performance required for real-time decoding of neural signals to enable users with disability to control enabling, mobile assistive devices.