Protocol to decode representations from EEG data with intermixed signals using temporal signal decomposition and multivariate pattern-analysis

The electroencephalogram (EEG) is one of the most widely used techniques in cognitive neuroscience. We present a protocol showing how to combine a temporal signal decomposition approach (RIDE, Residue iteration decomposition) with multivariate pattern analysis (MVPA) to obtain insights into the temp...

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Autores principales: Takács, Ádám, Yu, Shijing, Mückschel, Moritz, Beste, Christian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Protocol
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9168732/
https://www.ncbi.nlm.nih.gov/pubmed/35677605
http://dx.doi.org/10.1016/j.xpro.2022.101399
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author Takács, Ádám
Yu, Shijing
Mückschel, Moritz
Beste, Christian
author_facet Takács, Ádám
Yu, Shijing
Mückschel, Moritz
Beste, Christian
author_sort Takács, Ádám
collection PubMed
description The electroencephalogram (EEG) is one of the most widely used techniques in cognitive neuroscience. We present a protocol showing how to combine a temporal signal decomposition approach (RIDE, Residue iteration decomposition) with multivariate pattern analysis (MVPA) to obtain insights into the temporal stability of representations coded in distinct informational fractions of the EEG signal. In this protocol, we describe pre-processing of human EEG data, followed by the set-up and use of MATLAB-based toolboxes for RIDE and MVPA analysis. For complete details on the use and execution of this protocol, please refer to Petruo et al. (2021).
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spelling pubmed-91687322022-06-07 Protocol to decode representations from EEG data with intermixed signals using temporal signal decomposition and multivariate pattern-analysis Takács, Ádám Yu, Shijing Mückschel, Moritz Beste, Christian STAR Protoc Protocol The electroencephalogram (EEG) is one of the most widely used techniques in cognitive neuroscience. We present a protocol showing how to combine a temporal signal decomposition approach (RIDE, Residue iteration decomposition) with multivariate pattern analysis (MVPA) to obtain insights into the temporal stability of representations coded in distinct informational fractions of the EEG signal. In this protocol, we describe pre-processing of human EEG data, followed by the set-up and use of MATLAB-based toolboxes for RIDE and MVPA analysis. For complete details on the use and execution of this protocol, please refer to Petruo et al. (2021). Elsevier 2022-06-02 /pmc/articles/PMC9168732/ /pubmed/35677605 http://dx.doi.org/10.1016/j.xpro.2022.101399 Text en © 2022. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Protocol
Takács, Ádám
Yu, Shijing
Mückschel, Moritz
Beste, Christian
Protocol to decode representations from EEG data with intermixed signals using temporal signal decomposition and multivariate pattern-analysis
title Protocol to decode representations from EEG data with intermixed signals using temporal signal decomposition and multivariate pattern-analysis
title_full Protocol to decode representations from EEG data with intermixed signals using temporal signal decomposition and multivariate pattern-analysis
title_fullStr Protocol to decode representations from EEG data with intermixed signals using temporal signal decomposition and multivariate pattern-analysis
title_full_unstemmed Protocol to decode representations from EEG data with intermixed signals using temporal signal decomposition and multivariate pattern-analysis
title_short Protocol to decode representations from EEG data with intermixed signals using temporal signal decomposition and multivariate pattern-analysis
title_sort protocol to decode representations from eeg data with intermixed signals using temporal signal decomposition and multivariate pattern-analysis
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9168732/
https://www.ncbi.nlm.nih.gov/pubmed/35677605
http://dx.doi.org/10.1016/j.xpro.2022.101399
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