Although widely employed in medical diagnostic applications, most of the available commercial ultrasound (US) scanners do not always fit the needs of research users. Access to raw US data, portability, flexibility and advanced user control are essential features to explore alternative biomedical signal and imaging processing algorithms. In this paper, we present the initial results of a reconfigurable, cost-effective and modular 128-channel FPGA and PC-based US system, specifically designed for teaching and medical imaging research. The proposed system exploits the advantages of the MD2131 (Microchip Technology Inc.) beamformer source driver to generate arbitrary waveforms and the analog front-end AFE5805 (Texas Instruments Inc.) to obtain the maximum flexibility and wide data access to the various US data streams. Two applications involving plane wave excitation and delay-and-sum (DAS) beamforming are discussed. The results show that the open platform can help biomedical students and researchers to develop and evaluate different imaging strategies for medical US imaging and nondestructive testing (NDT) techniques, among other applications.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1109/EMBC.2016.7591892 | DOI Listing |
IEEE Trans Biomed Circuits Syst
August 2023
Recent translational efforts in brain-machine interfaces (BMI) are demonstrating the potential to help people with neurological disorders. The current trend in BMI technology is to increase the number of recording channels to the thousands, resulting in the generation of vast amounts of raw data. This in turn places high bandwidth requirements for data transmission, which increases power consumption and thermal dissipation of implanted systems.
View Article and Find Full Text PDFObjective: We present Myolink, a portable, modular, low-noise electrophysiology amplifier optimized for high-density surface electromyogram (HD sEMG) acquisition.
Methods: Myolink consists of 4 modules. Each 10 × 8 cm module can concurrently acquire 32 unipolar electrode potentials at sampling rates of up to 8 kHz with 24-bit resolution.
J Neural Eng
February 2022
Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom.
Various on-workstation neural-spike-based brain machine interface (BMI) systems have reached the point of in-human trials, but on-node and on-implant BMI systems are still under exploration. Such systems are constrained by the area and battery. Researchers should consider the algorithm complexity, available resources, power budgets, CMOS technologies, and the choice of platforms when designing BMI systems.
View Article and Find Full Text PDFIEEE Trans Biomed Eng
January 2021
Objective: Spiking activity of individual neurons can be separated from the acquired multi-unit activity with spike sorting methods. Processing the recorded high-dimensional neural data can take a large amount of time when performed on general-purpose computers.
Methods: In this paper, an FPGA-based real-time spike sorting system is presented which takes into account the spatial correlation between the electrical signals recorded with closely-packed recording sites to cluster multi-channel neural data.
IEEE Trans Neural Syst Rehabil Eng
December 2017
A multichannel neural interface system is an important tool for various types of neuroscientific studies. For the electrical interface with a biological system, high-precision high-speed data recording and various types of stimulation capability are required. In addition, real-time signal processing is an important feature in the implementation of a real-time closed-loop system without unwanted substantial delay for feedback stimulation.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!