Detection and classification of electroneurogram (ENG) signals in the peripheral nervous system can be achieved by velocity selective recording (VSR) using multi-electrode arrays. This paper describes an implantable VSR-based ENG recording system representing a significant development in the field since it is the first system of its type that can record naturally evoked ENG and be interfaced wirelessly using a low data rate transcutaneous link. The system consists of two CMOS ASICs one of which is placed close to the multi-electrode cuff array (MEC), whilst the other is mounted close to the wireless link. The digital ASIC provides the signal processing required to detect selectively ENG signals based on velocity. The design makes use of an original architecture that is suitable for implantation and reduces the required data rate for transmission to units placed outside the body. Complete measured electrical data from samples of the ASICs are presented that show that the system has the capability to record signals of amplitude as low as 0.5 μV, which is adequate for the recording of naturally evoked ENG. In addition, measurements of electrically evoked ENG from the explanted sciatic nerves of Xenopus Laevis frogs are presented.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1007/s11517-016-1567-9 | DOI Listing |
Med Biol Eng Comput
December 2024
School of Mechanical Engineering, Yanshan University, Qinhuangdao, China.
This study focuses on improving the performance of steady-state visual evoked potential (SSVEP) in brain-computer interfaces (BCIs) for robotic control systems. The challenge lies in effectively reducing the impact of artifacts on raw data to enhance the performance both in quality and reliability. The proposed MVMD-MSI algorithm combines the advantages of multivariate variational mode decomposition (MVMD) and multivariate synchronization index (MSI).
View Article and Find Full Text PDFJ Neural Eng
December 2024
Stanford University, 452 Lomita Mall, Stanford, California, 94305, UNITED STATES.
Objective: Neural interfaces are designed to evoke specific patterns of electrical activity in populations of neurons by stimulating with many electrodes. However, currents passed simultaneously through multiple electrodes often combine nonlinearly to drive neural responses, making evoked responses difficult to predict and control. This response nonlinearity could arise from the interaction of many excitable sites in each cell, any of which can produce a spike.
View Article and Find Full Text PDFJ Neural Eng
December 2024
Department of Automation and Applied Informatics, Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics, Műegyetem rkp. 3., Budapest, 1111, HUNGARY.
Objective: The development of deep learning models for electroencephalography (EEG) signal processing is often constrained by the limited availability of high-quality data. Data augmentation techniques are among the solutions to overcome these challenges, and deep neural generative models, with their data synthesis capabilities, are potential candidates.
Approach: The current work investigates enhanced diffusion probabilistic models (DPM) and sampling methods for brain signal generation and data augmentation.
IEEE Open J Eng Med Biol
July 2024
Scuola Superiore Sant'Anna Pisa 56127 Italy.
The objective of this study was to characterize hemodynamic changes during trans-vascular stimulation of the renal nerve and their dependence on stimulation parameters. We employed a stimulation catheter inserted in the right renal artery under fluoroscopic guidance, in pigs. Systolic, diastolic and pulse blood pressure and heart rate were recorded during stimulations delivered at different intravascular sites along the renal artery or while varying stimulation parameters (amplitude, frequency, and pulse width).
View Article and Find Full Text PDFJ Neural Eng
December 2024
Biomedical Sciences and Biomedical Engineering Division, School of Biological Sciences, University of Reading, Reading, United Kingdom.
Cognition is achieved through communication between brain regions. Consequently, there is considerable interest in measuring effective connectivity. A promising effective connectivity metric is transcranial magnetic stimulation (TMS) evoked potentials (TEPs), an inflection in amplitude of the electroencephalogram recorded from one brain region as a result of TMS applied to another region.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!