Low-power lossless data compression for wireless brain electrophysiology

This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications.

Bibliographic Details
Authors: Cuevas López, Aarón, Pérez-Montoyo, Elena, López-Madrona, Víctor J., Canals, Santiago, Moratal, David
Format: article
Status:Published version
Publication Date:2022
Country:España
Institution:Consejo Superior de Investigaciones Científicas (CSIC)
Repository:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/286262
Online Access:http://hdl.handle.net/10261/286262
Access Level:Open access
Keyword:Low power
FPGA
Data compression
Electrophysiology
Wireless
Brain
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spelling Low-power lossless data compression for wireless brain electrophysiologyCuevas López, AarónPérez-Montoyo, ElenaLópez-Madrona, Víctor J.Canals, SantiagoMoratal, DavidLow powerFPGAData compressionElectrophysiologyWirelessBrainThis article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications.Wireless electrophysiology opens important possibilities for neuroscience, especially for recording brain activity in more natural contexts, where exploration and interaction are not restricted by the usual tethered devices. The limiting factor is transmission power and, by extension, battery life required for acquiring large amounts of neural electrophysiological data. We present a digital compression algorithm capable of reducing electrophysiological data to less than 65.5% of its original size without distorting the signals, which we tested in vivo in experimental animals. The algorithm is based on a combination of delta compression and Huffman codes with optimizations for neural signals, which allow it to run in small, low-power Field-Programmable Gate Arrays (FPGAs), requiring few hardware resources. With this algorithm, a hardware prototype was created for wireless data transmission using commercially available devices. The power required by the algorithm itself was less than 3 mW, negligible compared to the power saved by reducing the transmission bandwidth requirements. The compression algorithm and its implementation were designed to be device-agnostic. These developments can be used to create a variety of wired and wireless neural electrophysiology acquisition systems with low power and space requirements without the need for complex or expensive specialized hardware.Peer reviewedMultidisciplinary Digital Publishing InstituteConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202320232022info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/286262reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttps://doi.org/10.3390/s22103676Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2862622026-05-22T06:33:51Z
dc.title.none.fl_str_mv Low-power lossless data compression for wireless brain electrophysiology
title Low-power lossless data compression for wireless brain electrophysiology
spellingShingle Low-power lossless data compression for wireless brain electrophysiology
Cuevas López, Aarón
Low power
FPGA
Data compression
Electrophysiology
Wireless
Brain
title_short Low-power lossless data compression for wireless brain electrophysiology
title_full Low-power lossless data compression for wireless brain electrophysiology
title_fullStr Low-power lossless data compression for wireless brain electrophysiology
title_full_unstemmed Low-power lossless data compression for wireless brain electrophysiology
title_sort Low-power lossless data compression for wireless brain electrophysiology
dc.creator.none.fl_str_mv Cuevas López, Aarón
Pérez-Montoyo, Elena
López-Madrona, Víctor J.
Canals, Santiago
Moratal, David
author Cuevas López, Aarón
author_facet Cuevas López, Aarón
Pérez-Montoyo, Elena
López-Madrona, Víctor J.
Canals, Santiago
Moratal, David
author_role author
author2 Pérez-Montoyo, Elena
López-Madrona, Víctor J.
Canals, Santiago
Moratal, David
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Low power
FPGA
Data compression
Electrophysiology
Wireless
Brain
topic Low power
FPGA
Data compression
Electrophysiology
Wireless
Brain
description This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications.
publishDate 2022
dc.date.none.fl_str_mv 2022
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/286262
url http://hdl.handle.net/10261/286262
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://doi.org/10.3390/s22103676
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812429

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