Characterization of tfLIFE Neural Response for the Control of a Cybernetic Hand

Abstract

The development of interfaces that link the human nervous system with robotic devices or man-made devices has been a main area of research for several groups in the world. These groups focus on the restoration of motor and sensory function to those with degenerative diseases, injury or in amputees. A key component is these systems is a fast, intuitive, bidirectional interface between the biological and mechatronic systems that allows the robotic limb to be controlled as if it were a natural part of the body. Current hand prostheses use electromyographic (EMG) signals, but are limited to a small number of channels and to sensing volition. To achieve sensory feedback and a higher number of control channels, a neuroprosthetic interface are required. In the present study, thin-film longitudinal intra-fascicular electrodes (tfLIFE) were implanted in the sciatic nerve of the rabbit. Various sensory stimuli were applied to the hind limb of the rabbit and the elicited signals were recorded using the tfLIFEs. These signals were processed to determine whether the different modes of information could be decoded. Signals were Kalman filtered, wavelet denoised, and spike sorted. The classes of spikes found were then used to infer the stimulus applied to the rabbit. Although the signals acquired from a single tLIFE gave poor stimulus recognition, the combination of the signals from multiple sites led to better results. The spike sorting algorithm is also helped by the use of temporal correlation between the channels. A direct outcome of the results is the possibility of increasing the number of channels of control possible with a prosthetic limb