Abstract
Interfaces with the peripheral nerve provide the ability to extract motor activation and restore sensation to amputee patients. The ability to chronically extract motor activations from the peripheral nervous system remains an unsolved problem. In this study, chronic recordings with the Flat Interface Nerve Electrode (FINE) are employed to recover the activation levels of innervated muscles. The FINEs were implanted on the sciatic nerves of canines, and neural recordings were obtained as the animal walked on a treadmill. During these trials, electromyograms (EMG) from the surrounding hamstring muscles were simultaneously recorded and the neural recordings are shown to be free of interference or crosstalk from these muscles. Using a novel Bayesian algorithm, the signals from individual fascicles were recovered and then compared to the corresponding target EMG of the lower limb. High correlation coefficients (0.84 ± 0.07 and 0.61 ± 0.12) between the extracted tibial fascicle/medial gastrocnemius and peroneal fascicle/tibialis anterior muscle were obtained. Analysis calculating the information transfer rate (ITR) from the muscle to the motor predictions yielded approximately 5 and 1 bit per second (bps) for the two sources. This method can predict motor signals from neural recordings and could be used to drive a prosthesis by interfacing with residual nerves.
Highlights
The only available commercial approach is myoelectric control, in which the electromyograms (EMG) of residual muscles in the arm control the prosthetic limb
Interfacing directly with the nervous system offers the ability to recover motor activation as well as restore sensation with a single device, as early work in the field showed that residual nerves retained functional motor and sensory connections years after amputation[5]
The Flat Interface Nerve Electrode (FINE), which increases the surface area to volume ratio by gently reshaping the nerve into an ellipse to decrease the distance between the fascicles and an array of electrodes, was used to interface with peripheral nerves
Summary
The only available commercial approach is myoelectric control, in which the electromyograms (EMG) of residual muscles in the arm control the prosthetic limb. The functionality of this control method is greatly reduced when the amputation is above the elbow[3], advanced machine learning approaches may alleviate this problem[4] Such devices have no innate way of restoring sensation to the user beyond sensory substitution. Interfacing directly with the nervous system offers the ability to recover motor activation as well as restore sensation with a single device, as early work in the field showed that residual nerves (and cortical areas) retained functional motor and sensory connections years after amputation[5]. We report data showing that a single 16-ch FINE can accurately record nerve signals during normal gait, separate the activity from the main fascicles and selectively predict the activation of two innervated muscles using 100 ms windows, which could more readily translate to a real-time control scheme. This study demonstrates for the first time that voluntary motor signals correlated with muscle activity can be obtained from a peripheral nerve with a nerve computer interface
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