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Abstract
Proprioceptive afferent activities could be useful for providing sensory feedback signals for closed-loop control during functional electrical stimulation (FES). However, most previous studies have used the single-unit activity of individual neurons to extract sensory information from proprioceptive afferents. This study proposes a new decoding method to estimate ankle and knee joint angles using multiunit activity data. Proprioceptive afferent signals were recorded from a dorsal root ganglion with a single-shank microelectrode during passive movements of the ankle and knee joints, and joint angles were measured as kinematic data. The mean absolute value (MAV) was extracted from the multiunit activity data, and a dynamically driven recurrent neural network (DDRNN) was used to estimate ankle and knee joint angles. The multiunit activity-based MAV feature was sufficiently informative to estimate limb states, and the DDRNN showed a better decoding performance than conventional linear estimators. In addition, processing time delay satisfied real-time constraints. These results demonstrated that the proposed method could be applicable for providing real-time sensory feedback signals in closed-loop FES systems.
Highlights
A neural interface is a useful technology for extracting sensory information from the nervous system and providing feedback signals for use in functional electrical stimulation (FES) systems
When a multichannel microelectrode is used to record neural signals from a dorsal root ganglion, the recordings of proprioceptive afferent signals have been found to depend on stochastic variables, such as the positioning of the electrode and the distribution of the sensory neurons[20]
Robust and reliable feedback information regarding the limb state is required for closed-loop control in FES systems
Summary
A neural interface is a useful technology for extracting sensory information from the nervous system and providing feedback signals for use in FES systems. These studies used superimposed neural activities to extract control signals from intracortical recordings and achieved a better decoding performance than methods using traditional spike sorting approaches Such multiunit activity-based decoding approaches are applicable for extracting sensory information from afferent signals to provide feedback signals for use in closed-loop FES systems. A method to eliminate stimulus artefacts is necessary to obtain interference-free neural signals Addressing these issues, the current study presents a multiunit activity-based decoding method for providing real-time limb-state feedback to allow for closed-loop control in FES systems. The processing time delay of the proposed method was enough to meet real-time constraints These results suggest that the proposed multiunit activity-based decoding method is a useful approach to providing limb-state feedback for closed-loop control in FES systems
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