GPU optimization of electroencephalogram analysis

Autores
Raimondo, Federico; Kamienkowski, Juan E.; Fernández Slezak, Diego
Año de publicación
2011
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Nowadays, with the advent of new non-invasive techniques of brain imaging, researchers have access to neural processes underlying the cognition in humans. One of the main challenges in this techniques is the detection of patterns in brain signals, generally very noisy and with artifacts inserted by vital signs. One of the most successful techniques for this is Independent Component Analysis which detects statistically independent components that are produced from different sources. These methods are very expensive in computational time, with many hours of processing for a single experiment. We analyzed this algorithm and detect two main types of operations: vector-matrix and matrix-matrix. We implemented an ad-hoc solution that executes on GPU and compared this with the original and CUBLAS versions. We obtained a 4x and 40x of performance increase of vector-matrix and matrix-matrix operations, respectively. These results are the first step towards real-time EEG processing which may produce a significant advance into BCI applications.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
Electroencephalogram analysis
GPU optimization
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/126113
Acceder
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spelling GPU optimization of electroencephalogram analysisRaimondo, FedericoKamienkowski, Juan E.Fernández Slezak, DiegoCiencias InformáticasElectroencephalogram analysisGPU optimizationNowadays, with the advent of new non-invasive techniques of brain imaging, researchers have access to neural processes underlying the cognition in humans. One of the main challenges in this techniques is the detection of patterns in brain signals, generally very noisy and with artifacts inserted by vital signs. One of the most successful techniques for this is Independent Component Analysis which detects statistically independent components that are produced from different sources. These methods are very expensive in computational time, with many hours of processing for a single experiment. We analyzed this algorithm and detect two main types of operations: vector-matrix and matrix-matrix. We implemented an ad-hoc solution that executes on GPU and compared this with the original and CUBLAS versions. We obtained a 4x and 40x of performance increase of vector-matrix and matrix-matrix operations, respectively. These results are the first step towards real-time EEG processing which may produce a significant advance into BCI applications.Sociedad Argentina de Informática e Investigación Operativa2011-08info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/126113enginfo:eu-repo/semantics/altIdentifier/url/https://40jaiio.sadio.org.ar/sites/default/files/T2011/HPC/693.pdfinfo:eu-repo/semantics/altIdentifier/issn/1851-9326info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T11:02:29Zoai:sedici.unlp.edu.ar:10915/126113Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 11:02:29.98SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv GPU optimization of electroencephalogram analysis
title GPU optimization of electroencephalogram analysis
spellingShingle GPU optimization of electroencephalogram analysis
Raimondo, Federico
Ciencias Informáticas
Electroencephalogram analysis
GPU optimization
title_short GPU optimization of electroencephalogram analysis
title_full GPU optimization of electroencephalogram analysis
title_fullStr GPU optimization of electroencephalogram analysis
title_full_unstemmed GPU optimization of electroencephalogram analysis
title_sort GPU optimization of electroencephalogram analysis
dc.creator.none.fl_str_mv Raimondo, Federico
Kamienkowski, Juan E.
Fernández Slezak, Diego
author Raimondo, Federico
author_facet Raimondo, Federico
Kamienkowski, Juan E.
Fernández Slezak, Diego
author_role author
author2 Kamienkowski, Juan E.
Fernández Slezak, Diego
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Electroencephalogram analysis
GPU optimization
topic Ciencias Informáticas
Electroencephalogram analysis
GPU optimization
dc.description.none.fl_txt_mv Nowadays, with the advent of new non-invasive techniques of brain imaging, researchers have access to neural processes underlying the cognition in humans. One of the main challenges in this techniques is the detection of patterns in brain signals, generally very noisy and with artifacts inserted by vital signs. One of the most successful techniques for this is Independent Component Analysis which detects statistically independent components that are produced from different sources. These methods are very expensive in computational time, with many hours of processing for a single experiment. We analyzed this algorithm and detect two main types of operations: vector-matrix and matrix-matrix. We implemented an ad-hoc solution that executes on GPU and compared this with the original and CUBLAS versions. We obtained a 4x and 40x of performance increase of vector-matrix and matrix-matrix operations, respectively. These results are the first step towards real-time EEG processing which may produce a significant advance into BCI applications.
Sociedad Argentina de Informática e Investigación Operativa
description Nowadays, with the advent of new non-invasive techniques of brain imaging, researchers have access to neural processes underlying the cognition in humans. One of the main challenges in this techniques is the detection of patterns in brain signals, generally very noisy and with artifacts inserted by vital signs. One of the most successful techniques for this is Independent Component Analysis which detects statistically independent components that are produced from different sources. These methods are very expensive in computational time, with many hours of processing for a single experiment. We analyzed this algorithm and detect two main types of operations: vector-matrix and matrix-matrix. We implemented an ad-hoc solution that executes on GPU and compared this with the original and CUBLAS versions. We obtained a 4x and 40x of performance increase of vector-matrix and matrix-matrix operations, respectively. These results are the first step towards real-time EEG processing which may produce a significant advance into BCI applications.
publishDate 2011
dc.date.none.fl_str_mv 2011-08
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status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/126113
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dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://40jaiio.sadio.org.ar/sites/default/files/T2011/HPC/693.pdf
info:eu-repo/semantics/altIdentifier/issn/1851-9326
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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