Biology subjects

Rapeaux, A.

Publications and source records attributed to Rapeaux, A..

5 recordsLinked to original sources

bioRxiv2025

Neuromodulation of a peripheral nerve using fully polymeric cuff electrodes: Understanding predictability of selective stimulation

Peripheral nerve stimulation (PNS) offers therapeutic benefits across numerous clinical applications but suffers from limitations in high resolution spatial selectivity, especially in mixed nerves. This study presents a fully polymeric, transverse, multipolar nerve cuff made from a conductive elastomer (CE), designed for selective activation of individual fascicles. Fabricated with laser-based manufacturing techniques, the CE nerve cuff offers mechanical conformity and high charge injection capacity. Ex vivo experiments on the rat sciatic nerve demonstrate reliable compound action potential recordings and fascicular selectivity (SI > 0.65). The non-metal electrodes enable microCT-aided 3D reconstruction of nerve-electrode geometries without imaging artefacts, informing anatomically accurate simulations via the ASCENT pipeline. While in silico simulations predict some selective fascicular activation, discrepancies were observed between predicted and experimental selectivity magnitudes and electrode positions, particularly for the sural and tibial fascicles. The model was more sensitive to neuroanatomical variation than the experimental data, indicating limitations in current perineurium and CE electrode modelling assumptions. This work validates CE-based cuffs as viable alternatives to metallic devices for selective fascicular peripheral nerve activation and highlights the potential of imaging-informed simulations to optimize nerve interface design. Future improvements in electrode and nerve tissue modelling are needed to enhance in silico prediction accuracy and further advance spatially selective PNS technologies.

bioengineering↗

bioRxiv2022

Development of an Ultra Low-Cost SSVEP-based BCI Device for Real-Time On-Device Decoding

This study details the development of a novel, approx. {pound}20 electroencephalogram (EEG)-based brain-computer interface (BCI) intended to offer a financially and operationally accessible device that can be deployed on a mass scale to facilitate education and public engagement in the domain of EEG sensing and neurotechnologies. Real-time decoding of steady-state visual evoked potentials (SSVEPs) is achieved using variations of the widely-used canonical correlation analysis (CCA) algorithm: multi-set CCA and generalised CCA. All BCI functionality is executed on board an inexpensive ESP32 microcontroller. SSVEP decoding accuracy of 95.56 {+/-} 3.74% with an ITR of 102 bits/min was achieved with modest calibration.

bioengineering↗

bioRxiv2020

Towards robust, unobtrusive sensing of respiration using ultra-wideband impulse radar for the care of people living with dementia

The unobtrusive monitoring of vital signals and behaviour can be used to gather intelligence to support the care of people living with dementia. This can provide insights into the persons wellbeing and the neurogenerative process, as well as enable them to continue to live safely at home, thereby improving their quality of life. Within this context, this study investigated the deployability of non-contact respiration rate (RR) measurement based on an Ultra-Wideband (UWB) radar System-on-Chip (SoC). An algorithm was developed to simultaneously and continuously extract the respiration signal, together with the confidence level of the respiration signal and the target position, without needing any prior calibration. The radar-measured RR results were compared to the RR results obtained from a ground truth measure based on the breathing sound, and the error rates were within 8% with a mean value of 2.4%. The target localisation results match to the radar-to-chest distances with a mean error rate of 5.4%. The tested measurement range was up to 5m. The results suggest that the algorithm could perform sufficiently well in non-contact stationary respiration rate detection.

bioengineering↗

bioRxiv2020

A Block-Capable and Module-Extendable 4-Channel Stimulator for Acute Neurophysiology

ObjectiveThis paper describes the design, testing and use of a novel multichannel block-capable stimulator for acute neurophysiology experiments to study highly selective neural interfacing techniques. This paper demonstrates the stimulators ability to excite and inhibit nerve activity in the rat sciatic nerve model concurrently using monophasic and biphasic nerve stimulation as well as high-frequency alternating current (HFAC). ApproachThe proposed stimulator uses a Howland Current Pump circuit as the main analogue stimulator element. 4 current output channels with a common return path were implemented on printed circuit board using Commercial Off-The-Shelf components. Programmable operation is carried out by an ARM Cortex-M4 Microcontroller on the Freescale freedom development platform (K64F). Main ResultsThis stimulator design achieves {+/-}10 mA of output current with {+/-}15 V of compliance and less than 6 {micro}A of resolution using a quad-channel 12-bit external DAC, for four independently driven channels. This allows the stimulator to carry out both excitatory and inhibitory (HFAC block) stimulation. DC Output impedance is above 1 M{Omega}. Overall cost is less than USD 450 or GBP 350 and device size is approximately 9 cm x 6 cm x 5 cm. SignificanceExperimental neurophysiology often requires significant investment in bulky equipment for specific stimulation requirements, especially when using HFAC block. Different stimulators have limited means of communicating with each other, making protocols more complicated. This device provides an effective solution for multi-channel stimulation and block of nerves, enabling studies on selective neural interfacing in acute scenarios with an affordable, portable and space-saving design for the laboratory. The stimulator can be further upgraded with additional modules to extend functionality while maintaining straightforward programming and integration of functions with one controller.

bioengineering↗

bioRxiv2017

Recovery Dynamics of the High Frequency Alternating Current Nerve Block

ObjectiveHigh-Frequency alternating current (HFAC) nerve block has great potential for neuromodulation-based therapies. However nerve function recovery dynamics after a block is highly understudied. This study aims to characterise the recovery dynamics of neural function after an HFAC block.\n\nApproachExperiments were carried out in-vivo to determine blocking efficacy as a function of blocking signal amplitude and frequency, and recovery times as well as recovery completeness was measured within a 0.7 s time scale from the end of block. The sciatic nerve was stimulated at 100 Hz during recovery to reduce error to within {+/-}10 ms for measurements of recovery dynamics. The electromyogram (EMG) signals were measured from gastrocnemius medialis and tibialis anterior during trials as an indicator for nerve function.\n\nMain ResultsThe HFAC block was most reliable around 20 kHz, with block thresholds approximately 5 or 6 mA depending on the animal and muscle. Recovery times ranged from 20 to 430 milliseconds and final values spanned relative outputs from approximately 1 to 0.2. Higher blocking signal frequencies and amplitudes increased recovery time and decreased recovery completeness.\n\nSignificanceThese results confirm that recovery dynamics from block depend on blocking signal frequency and amplitude, which is of particular importance for neuromodulation therapies and for comparing results across studies using different blocking signal parameters.

neuroscience↗