Studying embedded human EEG dynamics using generative topographic mapping

A method has recently been proposed [1] to extract multiple signal source information from single-channel electroencephalogram (EEG) recordings. A dynamical systems approach is used to analyze the resulting EEG time series, and its dynamics are captured by the transformation of the original data int...

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Detalhes bibliográficos
Autores: Vellido Alcacena, Alfredo|||0000-0002-9843-1911, El-Deredy, W., Lisboa, Paulo J G
Tipo de documento: relatório científico
Data de publicação:2004
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositório:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglês
OAI Identifier:oai:upcommons.upc.edu:2117/97971
Acesso em linha:https://hdl.handle.net/2117/97971
Access Level:Acceso aberto
Palavra-chave:Topographic mapping
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
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spelling Studying embedded human EEG dynamics using generative topographic mappingVellido Alcacena, Alfredo|||0000-0002-9843-1911El-Deredy, W.Lisboa, Paulo J GTopographic mappingÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificialA method has recently been proposed [1] to extract multiple signal source information from single-channel electroencephalogram (EEG) recordings. A dynamical systems approach is used to analyze the resulting EEG time series, and its dynamics are captured by the transformation of the original data into an embedding matrix residing in a Euclidean embedding space. Measurements in [1] are taken to be of ongoing unbounded EEG recordings. Many experiments concerning the study of cognitive tasks, though, are developed in a multi-subject repetitive setting where time-boundaries are defined in relation to the onset time of certain stimuli. Each repetition of an experiment is known as a trial and, although the experimental setting might induce to expect little variability amongst responses, the reality usually yields high inter-trial and inter-subject variability. Pooling all responses may mislead their interpretation. In this paper we resort to the Generative Topographic Mapping (GTM, [2]), a neural-network inspired but statistically principled unsupervised model, to achieve the following goals: First, the definition of groups of trials with intra-group similarities and inter-group differences in order to improve the interpretability of the results in the aforementioned experimental settings; second, the visualization of embedded EEG dynamics in a 2-dimensional latent space; finally, the study of the trajectories of these EEG dynamics over the GTM latent space representation, showing that transitions and stationary states in these trajectories correspond to special features in the time-power and time-frequency representations of the EEG data.20042004-02-0120162016-12-12reporthttp://purl.org/coar/resource_type/c_93fcVoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/reportapplication/pdfhttps://hdl.handle.net/2117/97971reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/979712026-05-27T15:37:01Z
dc.title.none.fl_str_mv Studying embedded human EEG dynamics using generative topographic mapping
title Studying embedded human EEG dynamics using generative topographic mapping
spellingShingle Studying embedded human EEG dynamics using generative topographic mapping
Vellido Alcacena, Alfredo|||0000-0002-9843-1911
Topographic mapping
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
title_short Studying embedded human EEG dynamics using generative topographic mapping
title_full Studying embedded human EEG dynamics using generative topographic mapping
title_fullStr Studying embedded human EEG dynamics using generative topographic mapping
title_full_unstemmed Studying embedded human EEG dynamics using generative topographic mapping
title_sort Studying embedded human EEG dynamics using generative topographic mapping
dc.creator.none.fl_str_mv Vellido Alcacena, Alfredo|||0000-0002-9843-1911
El-Deredy, W.
Lisboa, Paulo J G
author Vellido Alcacena, Alfredo|||0000-0002-9843-1911
author_facet Vellido Alcacena, Alfredo|||0000-0002-9843-1911
El-Deredy, W.
Lisboa, Paulo J G
author_role author
author2 El-Deredy, W.
Lisboa, Paulo J G
author2_role author
author
dc.subject.none.fl_str_mv Topographic mapping
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
topic Topographic mapping
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
description A method has recently been proposed [1] to extract multiple signal source information from single-channel electroencephalogram (EEG) recordings. A dynamical systems approach is used to analyze the resulting EEG time series, and its dynamics are captured by the transformation of the original data into an embedding matrix residing in a Euclidean embedding space. Measurements in [1] are taken to be of ongoing unbounded EEG recordings. Many experiments concerning the study of cognitive tasks, though, are developed in a multi-subject repetitive setting where time-boundaries are defined in relation to the onset time of certain stimuli. Each repetition of an experiment is known as a trial and, although the experimental setting might induce to expect little variability amongst responses, the reality usually yields high inter-trial and inter-subject variability. Pooling all responses may mislead their interpretation. In this paper we resort to the Generative Topographic Mapping (GTM, [2]), a neural-network inspired but statistically principled unsupervised model, to achieve the following goals: First, the definition of groups of trials with intra-group similarities and inter-group differences in order to improve the interpretability of the results in the aforementioned experimental settings; second, the visualization of embedded EEG dynamics in a 2-dimensional latent space; finally, the study of the trajectories of these EEG dynamics over the GTM latent space representation, showing that transitions and stationary states in these trajectories correspond to special features in the time-power and time-frequency representations of the EEG data.
publishDate 2004
dc.date.none.fl_str_mv 2004
2004-02-01
2016
2016-12-12
dc.type.none.fl_str_mv report
http://purl.org/coar/resource_type/c_93fc
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/report
format report
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/97971
url https://hdl.handle.net/2117/97971
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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