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Improving detection accuracy of memristor-based bio-signal sensing platform

Improving detection accuracy of memristor-based bio-signal sensing platform
Improving detection accuracy of memristor-based bio-signal sensing platform
Recently 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 redsignaluced 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, principal component analysis.
1932-4545
203-211
Gupta, Isha
11f9ea1a-e38a-45d4-930d-96ac78b3d734
Prodromakis, Themistoklis
d58c9c10-9d25-4d22-b155-06c8437acfbf
Serb, Alexantrou
30f5ec26-f51d-42b3-85fd-0325a27a792c
Khiat, Ali
bf549ddd-5356-4a7d-9c12-eb6c0d904050
Gupta, Isha
11f9ea1a-e38a-45d4-930d-96ac78b3d734
Prodromakis, Themistoklis
d58c9c10-9d25-4d22-b155-06c8437acfbf
Serb, Alexantrou
30f5ec26-f51d-42b3-85fd-0325a27a792c
Khiat, Ali
bf549ddd-5356-4a7d-9c12-eb6c0d904050

Gupta, Isha, Prodromakis, Themistoklis, Serb, Alexantrou and Khiat, Ali (2017) Improving detection accuracy of memristor-based bio-signal sensing platform. IEEE Transactions on Biomedical Circuits and Systems, 11 (1), 203-211. (doi:10.1109/TBCAS.2016.2580499).

Record type: Article

Abstract

Recently 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 redsignaluced 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, principal component analysis.

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More information

Accepted/In Press date: 2 June 2016
e-pub ahead of print date: 19 August 2016
Published date: February 2017
Organisations: Nanoelectronics and Nanotechnology, Electronics & Computer Science

Identifiers

Local EPrints ID: 411020
URI: https://eprints.soton.ac.uk/id/eprint/411020
ISSN: 1932-4545
PURE UUID: f1f2d234-60c5-4e76-a04b-b4ccdf87d32e
ORCID for Themistoklis Prodromakis: ORCID iD orcid.org/0000-0002-6267-6909

Catalogue record

Date deposited: 13 Jun 2017 16:31
Last modified: 03 Dec 2019 01:36

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