Simulated Nerve Signal Generation for Multi-electrode Cuff System Testing

Al-Shueli, Assad; Clarke, Chris; Taylor, J.

doi:10.1109/icbeb.2012.354

Cited by 6 publications

(10 citation statements)

References 16 publications

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“…The main benefit of this technique is to reduce process time using FPGA parallel processing ability and minimise the hardware size and power usage. Further, the objective design has been tested and ran on FPGA platform using artificially generated nerve signals already designed by the previous study [25]. Figure 1 illustrates the overall design component for the complete system which is consist from four units.…”

Section: Ai Al-shuelimentioning

confidence: 99%

Optimised Design and Implementation of Band-Pass Filter for Neural Recording System

Al-shueli*

2020

IJRTE

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In this paper, an eight order efficient digital infinite impulse response filter is designed to improve the signal to noise ratio (SNR) and minimise the hardware and power consumption. For this task, an optimisation method has been adapted to reduce the root mean square error and hardware usage. The filter has been designed and analysed using Matlab and Modelsim, the implementation has been synthesis on Xilinx Spartan 3E-100 (xc3s100e) field-programmable gate array board. Moreover, an optimisation process using parallel algorithm has bee adapted for further reduction in the hardware area and power consumption. -Iraq, 1976) is an assist prof. in electronics engineering at University of Thi-Qar/Iraq. His B.Sc. degree in electrical engineering and his M.Sc. degree in electronic and communication engineering were granted from University of Mustansiriyah, Baghdad, Iraq in 1998 and 2000 respectively. Later he was awarded his PhD degree in electronic and electrical engineering from University of Bath, Bath, UK, in 2013. His research interests are electrical resistivity, digital filter design, microelectronic design, digital signal processing. Dr. Al-shueli is a member in IEEE organization, Iraq Section, as well as a member in the Iraqi Engineering Union. The results show the Band Pass Filter effectively functions in real time recording application with significant improvement in the SNR which could achieve high-velocity selective resolution. The present work offers a structure of implementing a band-pass filter on FPGAs using a nonlinear digital filter shows a significant saving of 25.4% in power consumption and 29.9% of the hardware size comparing with the latest algorithm of IIR filter design. Consequently, this is an essential development to enhance the neural signals to be adopted as reference or control signals in artificial limbs devices.

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Section: Ai Al-shuelimentioning

confidence: 99%

Optimised Design and Implementation of Band-Pass Filter for Neural Recording System

Al-shueli*

2020

IJRTE

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“…The first stage on the left (block 1) is an FPGA-based programmable AP and noise signal generator (synthesiser) whose output is eleven unipolar signals delayed so as to simulate an AP moving through the cuff [19]. The signals are generated using a modified pulsewidth modulation output direct from the FPGA.…”

Section: A System Description 1) Conventional Vsr Signal Processing:mentioning

confidence: 99%

“…The AP generator was described in [19] and so only a summary will be given here. The input to the MEC is a transmembrane action potential function (TMAP), V m (t) and the resulting single fibre action potential is a propagating wave with the time dependence of the underlying TMAP function and the spatial properties of the MEC [2].…”

Section: ) Action Potential (Ap) and Noise Generationmentioning

confidence: 99%

“…The parameter values in eqn (1) The AP signal generation system is implemented as an FPGA controller plus a few passive components. It employs pulse-width modulation and related methods to achieve a very smooth representation of V m (t) without the need for excessively high sample rates and/or high resolution digital components [19]. For the purposes of this study, the system is modified to allow variable amounts of noise to be added to each channel.…”

Section: ) Action Potential (Ap) and Noise Generationmentioning

confidence: 99%

“…The approach is somewhat analogous to that employed in speech recognition and hence the chosen type of ANN, a time-delay neural network, was first employed in that application [18]. This paper consists of a study based on ENG data generated using a specially designed synthesised source [19] that permits the addition of controlled amounts of white Gaussian noise. The behaviour of the ANN-based velocity classifier is examined as the noise level is increased and the form of the AP input is varied from the pattern used for training.…”

Section: Introductionmentioning

confidence: 99%

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