This application addresses broad Challenge Area (06) Enabling Technologies and specific Challenge Topic, 06-HD-101 Improved interfaces for prostheses to improve rehabilitation outcomes. Considerable progress has been made during the last decade in the development of BMIs -- devices that translate brain activity into the commands to artificial actuators, such as prosthetic limbs. Neuroprosthetics based on BMI technology aim to restore limb mobility in people suffering from brain injury, neurological diseases and limb loss. One significant unknown in BMI research is the issue of providing sensory feedback from neuroprosthetics to their users. This gap in the field's knowledge impedes the development of clinically relevant neuroprosthetics because normal motor behaviors, which neuroprosthetic systems strive to reproduce, critically depend on the sensory feedback from the skin, muscle and tendon receptors. To address this problem, we propose developing a sensorized neuroprosthesis in which the signals from touch and pressure sensors of a prosthetic hand are delivered to the brain as spatiotemporal patterns of ICMS of the primary somatosensory cortex. We are well positioned to conduct these experiments and achieve success because of our extensive experience with monkey models of BMIs and previous work in which we used spatiotemporal cortical stimulation to guide animal motor performance. The experiments will be conducted in rhesus macaques trained to grip objects initially using a virtual-environment arm and then a robotic exoskeleton encasing the monkey's own arm. These monkeys will be chronically implanted with multielectrode arrays in cortical motor and somatosensory areas. BMI decoding algorithms will extract motor commands from the neural activity recorded by motor cortex implants. These commands will be used to enact reaching and gripping performed by artificial actuators. Sensations of touch, pressure and texture will be mimicked by spatiotemporal patterns of ICMS delivered through the somatosensory cortex implants.
This study is expected to have a significant impact on the development of clinical neuroprosthetics because it will produce effective techniques for sensorizing prosthetic limbs, making them feel as parts of the body and improving the accuracy of manual operations.
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