Exploring the 'EarSwitch' concept: a novel ear based control method for assistive technology.

Background: Loss of communication with loved ones and carers is one of the most isolating and debilitating effects of many neurological disorders. Assistive technology (AT) supports individuals with communication, but the acceptability of AT solutions is highly variable. In this paper a novel ear based control method of AT, the concept of 'EarSwitch', is presented.

Baseline cystometric parameters in conscious and anesthetized sheep: experimental data and systematic review.

Objective: To characterize cystometry in conscious and anesthetized sheep, including bladder response to sacral root electrical stimulation, thereby providing a baseline set of values.

Methods: Single-fill cystometries were repeated in adult mule ewes both conscious (n = 5) and under general anesthesia (18) using a commercial system. Parameters including bladder capacity, detrusor (bladder) pressure, urethral opening pressure, bladder compliance, number of nonvoiding detrusor contractions, and bladder pressure change in response to electrical stimulation of the sacral roots under general anesthesia are reported.

A Scoping Review of Machine Learning Applied to Peripheral Nerve Interfaces.

Peripheral nerve interfaces (PNIs) can enable communication with the peripheral nervous system and have a broad range of applications including in bioelectronic medicine and neuroprostheses. They can modulate neural activity through stimulation or monitor conditions by recording from the peripheral nerves. The recent growth of Machine Learning (ML) has led to the application of a wide variety of ML techniques to PNIs, especially in circumstances where the goal is classification or regression.

A Review of Motor Brain-Computer Interfaces Using Intracranial Electroencephalography Based on Surface Electrodes and Depth Electrodes.

Brain-computer interfaces (BCIs) provide a communication interface between the brain and external devices and have the potential to restore communication and control in patients with neurological injury or disease. For the invasive BCIs, most studies recruited participants from hospitals requiring invasive device implantation. Three widely used clinical invasive devices that have the potential for BCIs applications include surface electrodes used in electrocorticography (ECoG) and depth electrodes used in Stereo-electroencephalography (SEEG) and deep brain stimulation (DBS).

Amplified seasonality in western Europe in a warmer world.

Documenting the seasonal temperature cycle constitutes an essential step toward mitigating risks associated with extreme weather events in a future warmer world. The mid-Piacenzian Warm Period (mPWP), 3.3 to 3.

Ex-vivo systems for neuromodulation: A comparison of ex-vivo and in-vivo large animal nerve electrophysiology.

Background: Little research exists on extending ex-vivo systems to large animal nerves, and to the best of our knowledge, there has yet to be a study comparing these against in-vivo data. This paper details the first ex-vivo system for large animal peripheral nerves to be compared with in-vivo results.

New Method: Detailed ex-vivo and in-vivo closed-loop neuromodulation experiments were conducted on pig ulnar nerves.

Channel Selection for Stereo- Electroencephalography (SEEG)-Based Invasive Brain-Computer Interfaces Using Deep Learning Methods.

Brain-computer interfaces (BCIs) can enable direct communication with assistive devices by recording and decoding signals from the brain. To achieve high performance, many electrodes will be used, such as the recently developed invasive BCIs with channel numbers up to hundreds or even thousands. For those high-throughput BCIs, channel selection is important to reduce signal redundancy and invasiveness while maintaining decoding performance.

Data augmentation for invasive brain-computer interfaces based on stereo-electroencephalography (SEEG).

Deep learning is increasingly used for brain-computer interfaces (BCIs). However, the quantity of available data is sparse, especially for invasive BCIs. Data augmentation (DA) methods, such as generative models, can help to address this sparseness.

A Multielectrode Nerve Cuff for Chronic Velocity Selective Recording in a sheep model.

Supra-sacral spinal cord injury (SCI) causes loss of bladder fullness sensation and bladder over-activity, leading to retention and incontinence respectively. Velocity selective recording (VSR) of nerve roots innervating the bladder might enable identification of bladder activity. A 10-electrode nerve cuff for sacral nerve root VSR was developed and tested in a sheep model during acute surgeries and chronic implantation for 6 months.

Denoising and decoding spontaneous vagus nerve recordings with machine learning.

Neural interfaces that electrically stimulate the peripheral nervous system have been shown to successfully improve symptom management for several conditions, such as epilepsy and depression. A crucial part for closing the loop and improving the efficacy of implantable neuromodulation devices is the efficient extraction of meaningful information from nerve recordings, which can have a low Signal-to-Noise ratio (SNR) and non-stationary noise. In recent years, machine learning (ML) models have shown outstanding performance in regression and classification problems, but it is often unclear how to translate and assess these for novel tasks in biomedical engineering.

Data-driven digital health technologies in the remote clinical care of diabetic foot ulcers: a scoping review.

Background: The availability and effectiveness of Digital Health Technologies (DHTs) to support clinicians, empower patients, and generate economic savings for national healthcare systems are growing rapidly. Of particular promise is the capacity of DHTs to autonomously facilitate remote monitoring and treatment. Diabetic Foot Ulcers (DFUs) are characterised by high rates of infection, amputation, mortality, and healthcare costs.

Morphology and morphometry of the ulnar nerve in the forelimb of pigs.

The knowledge of the morphology and morphometry of peripheral nerves is essential for developing neural interfaces and understanding nerve regeneration in basic and applied research. Currently, the most adopted animal model is the rat, even though recent studies have suggested that the neuroanatomy of large animal models is more comparable to humans. The present knowledge of the morphological structure of large animal models is limited; therefore, the present study aims to describe the morphological characteristics of the Ulnar Nerve (UN) in pigs.

Using Kane's framework to build an assessment tool for undergraduate medical student's clinical competency with point of care ultrasound.

Introduction: Point-of-care ultrasonography (POCUS) is a portable imaging technology used in clinical settings. There is a need for valid tools to assess clinical competency in POCUS in medical students. The primary aim of this study was to use Kane's framework to evaluate an interpretation-use argument (IUA) for an undergraduate POCUS assessment tool.

Adaptation of the Two-CAP Method for Conduction Velocity Distribution Estimation in Multi-Channel Recordings.

Closed-loop neural interfaces capable of both stimulating and recording from peripheral nerves have the potential to enhance the long-term efficacy of neural implants. One challenge associated with closed loop interfaces is the accurate estimation of the distribution of active fibre conduction velocities (DCV) when recording the immediate effect of stimulation. DCV estimation has been performed in monopolar surface recordings using the Two-CAP method.

A comparison of extraneural approaches for selective recording in the peripheral nervous system.

The peripheral nervous system is a key target for the development of neural interfaces. However, recording from the peripheral nerves can be challenging especially when chronic implantation is desired. Nerve cuffs are frequently employed using either two or three contacts to provide a single recording channel.

An in-vitro system for closed loop neuromodulation of peripheral nerves.

Current neuromodulation research relies heavily on in-vivo animal experiments for developing novel devices and paradigms, which can be costly, time-consuming, and ethically contentious. As an alternative to this, in-vitro systems are being developed for examining explanted tissue in a controlled environment. However, these systems are typically tailored for cellular studies.

Pig Ulnar Nerve Recording with Sinusoidal and Temporal Interference Stimulation.

Temporal interference stimulation has been suggested as a method to reach deep targets during transcutaneous electrical stimulation. Despite its growing use in transcutaneous stimulation therapies, the mechanism of its operation is not fully understood. Recent efforts to fill that gap have focused on computational modelling, in vitro and in vivo experiments relying on physical observations - e.

A Comparison of Delay-and-Add and Maximum Likelihood Estimation for Velocity-Selective Recording Using Multi-Electrode Cuffs.

Extracting information from the peripheral nervous system with implantable devices remains a significant challenge that limits the advancement of closed-loop neural prostheses. Linear electrode arrays can record neural signals with both temporal and spatial selectivity, and velocity selective recording using the delay-and-add algorithm can enable classification based on fibre type. The maximum likelihood estimation method also measures velocity and is frequently used in electromyography but has never been applied to electroneurography.

Tutorial: a guide to techniques for analysing recordings from the peripheral nervous system.

The nervous system, through a combination of conscious and automatic processes, enables the regulation of the body and its interactions with the environment. The peripheral nervous system is an excellent target for technologies that seek to modulate, restore or enhance these abilities as it carries sensory and motor information that most directly relates to a target organ or function. However, many applications require a combination of both an effective peripheral nerve interface (PNI) and effective signal processing techniques to provide selective and stable recordings.

The Design of a Low Noise, Multi-Channel Recording System for Use in Implanted Peripheral Nerve Interfaces.

In the development of implantable neural interfaces, the recording of signals from the peripheral nerves is a major challenge. Since the interference from outside the body, other biopotentials, and even random noise can be orders of magnitude larger than the neural signals, a filter network to attenuate the noise and interference is necessary. However, these networks may drastically affect the system performance, especially in recording systems with multiple electrode cuffs (MECs), where a higher number of electrodes leads to complicated circuits.