Objective . Recording and stimulating from the peripheral nervous system are becoming important components in a new generation of bioelectronics systems. Although neurostimulation has seen a history of successful chronic applications in humans, peripheral nerve recording in humans chronically remains a challenge. Multi-contact nerve cuff electrode configurations have the potential to improve recording selectivity. We introduce the idea of using a convolutional neural network (CNN) to associate recordings of individual naturally evoked compound action potentials (CAPs) with neural pathways of interest, by exploiting the spatiotemporal patterns in multi-contact nerve cuff recordings. Approach . Nine Long-Evan rats were implanted with a 56-channel nerve cuff electrode on the sciatic nerve and afferent activity was selectively evoked in different fascicles (tibial, peroneal, sural) using mechanical stimuli. A recurrent neural network was then used to predict joint angles based on the predicted firing patterns from the CNN. Performance was measured based on the classification accuracy, F ₁ -score and the ability to track the ankle joint angle. Main results . Classification accuracy and F ₁ -score of the best CNN configuration were and 0.747 ± 0.114, respectively. The mean Pearson correlation coefficient between the manually measured ankle angle and the angle predicted from the estimated firing rate was Significance . The proposed method demonstrates that CAP-based classification can be achieved with high accuracy and can be used to track a physiological meaningful measure (e.g. joint angle). These results provide a promising direction for realizing more effective and intuitive neuroprosthetic systems.

Peripheral neural signals have the potential to provide the necessary motor, sensory or autonomic information for robust control in many neuroprosthetic and neuromodulation applications. However, developing methods to recover information encoded in these signals is a significant challenge. We introduce the idea of using spatiotemporal signatures extracted from multi-contact nerve cuff electrode recordings to classify naturally evoked compound action potentials (CAP). 9 Long-Evan rats were implanted with a 56-channel nerve cuff on the sciatic nerve. Afferent activity was selectively evoked in the different fascicles of the sciatic nerve (tibial, peroneal, sural) using mechano-sensory stimuli. Spatiotemporal signatures of recorded CAPs were used to train three different classifiers. Performance was measured based on the classification accuracy, F₁-score, and the ability to reconstruct original firing rates of neural pathways. The mean classification accuracies, for a 3-class problem, for the best performing classifier was 0.686 ± 0.126 and corresponding mean F₁-score was 0.605 ± 0.212. The mean Pearson correlation coefficients between the original firing rates and estimated firing rates found for the best classifier was 0.728 ± 0.276. The proposed method demonstrates the possibility of classifying individual naturally evoked CAPs in peripheral neural signals recorded from extraneural electrodes, allowing for more precise control signals in neuroprosthetic applications.

Objective. Extraction of information from the peripheral nervous system can provide control signals in neuroprosthetic applications. However, the ability to selectively record from different pathways within peripheral nerves is limited. We investigated the integration of spatial and temporal information for pathway discrimination in peripheral nerves using measurements from a multi-contact nerve cuff electrode. Approach. Spatiotemporal templates were established for different neural pathways of interest, and used to obtain tailored matched filters for each of these pathways. Simulated measurements of compound action potentials propagating through the nerve in different test cases were used to evaluate classification accuracy, percentage of missed spikes, and ability to reconstruct the original firing rates of the neural pathways. Main results. The mean Pearson correlation coefficients between the original firing rates and estimated firing rates over all tests cases was found to be 0.832 ± 0.161, 0.421 ± 0.145, 0.481 ± 0.340 for our algorithm, Bayesian spatial filters, and velocity selective recordings respectively. Significance. The proposed method shows that the spatiotemporal templates were able to provide more robust spike detection and reliable pathway discrimination than these existing algorithms.

Accurate simulations of peripheral nerve recordings are needed to develop improved neuroprostheses. Previous models of peripheral nerves contained simplifications whose effects have not been investigated. We created a novel detailed finite element (FE) model of a peripheral nerve, and used it to carry out a sensitivity analysis of several model parameters. To construct the model, in vivo recordings were obtained in a rat sciatic nerve using an 8-channel nerve cuff electrode, after which the nerve was imaged using magnetic resonance imaging (MRI). The FE model was constructed based on the MRI data, and included progressive branching of the fascicles. Neural pathways were defined in the model for the tibial, peroneal and sural fascicles. The locations of these pathways were selected so as to maximize the correlations between the simulated and in vivo recordings. The sensitivity analysis showed that varying the conductivities of neural tissues had little influence on the ability of the model to reproduce the recording patterns obtained experimentally. On the other hand, the increased anatomical detail did substantially alter the recording patterns observed, demonstrating that incorporating fascicular branching is an important consideration in models of nerve cuff recordings. The model used in this study constitutes an improved simulation tool and can be used in the design of neural interfaces.

The aim of this review is to identify gaps and provide a direction for future research in the utilization of Artificial Intelligence (AI) in chronic pain (CP) management. A comprehensive literature search was conducted using various databases, including Ovid MEDLINE, Web of Science Core Collection, IEEE Xplore, and ACM Digital Library. The search was limited to studies on AI in CP research, focusing on diagnosis, prognosis, clinical decision support, self-management, and rehabilitation. The studies were evaluated based on predefined inclusion criteria, including the reporting quality of AI algorithms used. After the screening process, 60 studies were reviewed, highlighting AI's effectiveness in diagnosing and classifying CP while revealing gaps in the attention given to treatment and rehabilitation. It was found that the most commonly used algorithms in CP research were support vector machines, logistic regression and random forest classifiers. The review also pointed out that attention to CP mechanisms is negligible despite being the most effective way to treat CP. The review concludes that to achieve more effective outcomes in CP management, future research should prioritize identifying CP mechanisms, CP management, and rehabilitation while leveraging a wider range of algorithms and architectures. This review highlights the potential of AI in improving the management of CP, which is a significant personal and economic burden affecting more than 30% of the world's population. The identified gaps and future research directions provide valuable insights to researchers and practitioners in the field, with the potential to improve healthcare utilization.

Background: Functional electrical stimulation therapy (FEST) is a promising intervention for the restoration of upper extremity function after cervical spinal cord injury (SCI). Objectives: This study describes and evaluates a novel FEST system designed to incorporate voluntary movement attempts and massed practice of functional grasp through the use of brain–computer interface (BCI) and computer vision (CV) modules. Methods: An EEG-based BCI relying on a single electrode was used to detect movement initiation attempts. A CV system identified the target object and selected the appropriate grasp type. The required grasp type and trigger command were sent to an FES stimulator, which produced one of four multichannel muscle stimulation patterns (precision, lateral, palmar, or lumbrical grasp). The system was evaluated with five neurologically intact participants and one participant with complete cervical SCI. Results: An integrated BCI-CV-FES system was demonstrated. The overall classification accuracy of th...