Abstract
BackgroundSeveral groups have shown that the performance of motor neuroprostheses can be significantly improved by detecting specific sensory events related to the ongoing motor task (e.g., the slippage of an object during grasping). Algorithms have been developed to achieve this goal by processing electroneurographic (ENG) afferent signals recorded by using single-channel cuff electrodes. However, no efforts have been made so far to understand the number and type of detectable sensory events that can be differentiated from whole nerve recordings using this approach.MethodsTo this aim, ENG afferent signals, evoked by different sensory stimuli were recorded using single-channel cuff electrodes placed around the sciatic nerve of anesthetized rats. The ENG signals were digitally processed and several features were extracted and used as inputs for the classification. The work was performed on integral datasets, without eliminating any noisy parts, in order to be as close as possible to real application.ResultsThe results obtained showed that single-channel cuff electrodes are able to provide information on two to three different afferent (proprioceptive, mechanical and nociceptive) stimuli, with reasonably good discrimination ability. The classification performances are affected by the SNR of the signal, which in turn is related to the diameter of the fibers encoding a particular type of neurophysiological stimulus.ConclusionsOur findings indicate that signals of acceptable SNR and corresponding to different physiological modalities (e.g. mediated by different types of nerve fibers) may be distinguished.
Highlights
IntroductionSeveral groups have shown that the performance of motor neuroprostheses can be significantly improved by detecting specific sensory events related to the ongoing motor task (e.g., the slippage of an object during grasping)
For all the datasets, the signal to noise ratio (SNR) was calculated as the ratio between the mean mean absolute value (MAV) amplitude of the ENG signals recorded whilst stimulating the animal hindpaw and the mean MAV amplitude recorded during absence of any stimulation, [21]: SNRdB
Proprioceptive stimulation provided the best SNR levels among the three stimuli, and tactile stimulus had better SNR compared to pain stimulation
Summary
Several groups have shown that the performance of motor neuroprostheses can be significantly improved by detecting specific sensory events related to the ongoing motor task (e.g., the slippage of an object during grasping). In most cases the use of recorded neural activity has been limited to sensory event onset detection for the closed-loop control of FES systems [13,14,15] and for the control of hand prostheses [16,17]. These limits can be partly overcome by using multi-site cuff electrodes [18], but it would still be important to enable strategies for discriminating sensory information that can be extracted from ENG signals recorded in a whole nerve using simple cuff electrodes
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