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Dataset for Improving detection accuracy of memristor-based biosignal sensing platform

Dataset for Improving detection accuracy of memristor-based biosignal sensing platform
Dataset for Improving detection accuracy of memristor-based biosignal sensing platform
Dataset supporting: Gupta I et al (2016) Improving Detection Accuracy of Memristor-Based Bio-Signal Sensing Platform. IEEE Transactions on Biomedical Circuits and Systems 11(1) 203 - 211Recently a novel neuronal activity sensor exploiting the intrinsic thresholded integrator capabilities of memristor devices has been proposed. Extracellular potentials captured by a standard bio-signal acquisition platform are fed into a memristive device which reacts to the input by changing its resistive state (RS) only when the signal ampitude exceeds a threshold. Thus, significant peaks in the neural signal can be stored as non-volatile changes in memristor resistive state whilst noise is effectively suppressed. However, as a memristor is subjected to increasing numbers of supra-threshold stimuli during practical operation, it accumulates (RS) changes and eventually saturates. This leads to severely reduced neural activity detection capabilities. In this work we explore different signal processing and memristor operating procedure strategies in order to improve the detection rate of significant neuronal activity events. We analyse the data obtained from a single-memristive device biased with a reference neural recording and observe that performance can be improved markedly by a) increasing the frequency at which the memristor is reset to an initial resistive state where it is known to be highly responsive, b) appropriately preconditioning the input waveform through application of gain and offset in order to optimally exploit the intrinsic device behaviour. All results are validated by benchmarking obtained spike detection performance against a state-of-the-art template matching system utilising computationally-heavy, multi-dimensional, principle component analysis.
University of Southampton
Gupta, Isha
11f9ea1a-e38a-45d4-930d-96ac78b3d734
Serb, Alexantrou
30f5ec26-f51d-42b3-85fd-0325a27a792c
Khiat, Ali
bf549ddd-5356-4a7d-9c12-eb6c0d904050
Prodromakis, Themistoklis
d58c9c10-9d25-4d22-b155-06c8437acfbf
Gupta, Isha
11f9ea1a-e38a-45d4-930d-96ac78b3d734
Serb, Alexantrou
30f5ec26-f51d-42b3-85fd-0325a27a792c
Khiat, Ali
bf549ddd-5356-4a7d-9c12-eb6c0d904050
Prodromakis, Themistoklis
d58c9c10-9d25-4d22-b155-06c8437acfbf

Gupta, Isha, Serb, Alexantrou, Khiat, Ali and Prodromakis, Themistoklis (2017) Dataset for Improving detection accuracy of memristor-based biosignal sensing platform. University of Southampton doi:10.5258/SOTON/D0111 [Dataset]

Record type: Dataset

Abstract

Dataset supporting: Gupta I et al (2016) Improving Detection Accuracy of Memristor-Based Bio-Signal Sensing Platform. IEEE Transactions on Biomedical Circuits and Systems 11(1) 203 - 211Recently a novel neuronal activity sensor exploiting the intrinsic thresholded integrator capabilities of memristor devices has been proposed. Extracellular potentials captured by a standard bio-signal acquisition platform are fed into a memristive device which reacts to the input by changing its resistive state (RS) only when the signal ampitude exceeds a threshold. Thus, significant peaks in the neural signal can be stored as non-volatile changes in memristor resistive state whilst noise is effectively suppressed. However, as a memristor is subjected to increasing numbers of supra-threshold stimuli during practical operation, it accumulates (RS) changes and eventually saturates. This leads to severely reduced neural activity detection capabilities. In this work we explore different signal processing and memristor operating procedure strategies in order to improve the detection rate of significant neuronal activity events. We analyse the data obtained from a single-memristive device biased with a reference neural recording and observe that performance can be improved markedly by a) increasing the frequency at which the memristor is reset to an initial resistive state where it is known to be highly responsive, b) appropriately preconditioning the input waveform through application of gain and offset in order to optimally exploit the intrinsic device behaviour. All results are validated by benchmarking obtained spike detection performance against a state-of-the-art template matching system utilising computationally-heavy, multi-dimensional, principle component analysis.

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More information

Published date: 2 June 2017
Organisations: Electronics & Computer Science, Nanoelectronics and Nanotechnology

Identifiers

Local EPrints ID: 410323
URI: https://eprints.soton.ac.uk/id/eprint/410323
PURE UUID: ed8cc6d3-ab26-4273-baa3-f0f0e9a82cee
ORCID for Themistoklis Prodromakis: ORCID iD orcid.org/0000-0002-6267-6909

Catalogue record

Date deposited: 07 Jun 2017 04:15
Last modified: 06 Jun 2018 12:24

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Contributors

Creator: Isha Gupta
Creator: Alexantrou Serb
Creator: Ali Khiat

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