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Analysis of instantaneous brain interactions contribution to a motor imagery classification task
The purpose of this study is to analyze the contribution of the interactions between electrodes, measured either as correlation or as Jaccard distance, to the classification of two actions in a motor imagery paradigm, namely, left-hand movement and right-hand movement. The analysis is performed in t...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9798002/ https://www.ncbi.nlm.nih.gov/pubmed/36589279 http://dx.doi.org/10.3389/fncom.2022.990892 |
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author | Cristancho Cuervo, Jorge Humberto Delgado Saa, Jaime F. Ripoll Solano, Lácides Antonio |
author_facet | Cristancho Cuervo, Jorge Humberto Delgado Saa, Jaime F. Ripoll Solano, Lácides Antonio |
author_sort | Cristancho Cuervo, Jorge Humberto |
collection | PubMed |
description | The purpose of this study is to analyze the contribution of the interactions between electrodes, measured either as correlation or as Jaccard distance, to the classification of two actions in a motor imagery paradigm, namely, left-hand movement and right-hand movement. The analysis is performed in two classifier models, namely, a static (linear discriminant analysis, LDA) model and a dynamic (hidden conditional random field, HCRF) model. The impact of using the sliding window technique (SWT) in the static and dynamic models is also analyzed. The study proved that their combination with temporal features provides significant information to improve the classification in a two-class motor imagery task for LDA (average accuracy: 0.7192 no additional features, 0.7617 by adding correlation, 0.7606 by adding Jaccard distance; p < 0.001) and HCRF (average accuracy: 0.7370 no additional features, 0.7764 by adding correlation, 0.7793 by adding Jaccard distance; p < 0.001). Also, we showed that adding interactions between electrodes improves significantly the performance of each classifier, regarding the nature of the interaction measure or the classifier itself. |
format | Online Article Text |
id | pubmed-9798002 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97980022022-12-30 Analysis of instantaneous brain interactions contribution to a motor imagery classification task Cristancho Cuervo, Jorge Humberto Delgado Saa, Jaime F. Ripoll Solano, Lácides Antonio Front Comput Neurosci Neuroscience The purpose of this study is to analyze the contribution of the interactions between electrodes, measured either as correlation or as Jaccard distance, to the classification of two actions in a motor imagery paradigm, namely, left-hand movement and right-hand movement. The analysis is performed in two classifier models, namely, a static (linear discriminant analysis, LDA) model and a dynamic (hidden conditional random field, HCRF) model. The impact of using the sliding window technique (SWT) in the static and dynamic models is also analyzed. The study proved that their combination with temporal features provides significant information to improve the classification in a two-class motor imagery task for LDA (average accuracy: 0.7192 no additional features, 0.7617 by adding correlation, 0.7606 by adding Jaccard distance; p < 0.001) and HCRF (average accuracy: 0.7370 no additional features, 0.7764 by adding correlation, 0.7793 by adding Jaccard distance; p < 0.001). Also, we showed that adding interactions between electrodes improves significantly the performance of each classifier, regarding the nature of the interaction measure or the classifier itself. Frontiers Media S.A. 2022-12-15 /pmc/articles/PMC9798002/ /pubmed/36589279 http://dx.doi.org/10.3389/fncom.2022.990892 Text en Copyright © 2022 Cristancho Cuervo, Delgado Saa and Ripoll Solano. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Cristancho Cuervo, Jorge Humberto Delgado Saa, Jaime F. Ripoll Solano, Lácides Antonio Analysis of instantaneous brain interactions contribution to a motor imagery classification task |
title | Analysis of instantaneous brain interactions contribution to a motor imagery classification task |
title_full | Analysis of instantaneous brain interactions contribution to a motor imagery classification task |
title_fullStr | Analysis of instantaneous brain interactions contribution to a motor imagery classification task |
title_full_unstemmed | Analysis of instantaneous brain interactions contribution to a motor imagery classification task |
title_short | Analysis of instantaneous brain interactions contribution to a motor imagery classification task |
title_sort | analysis of instantaneous brain interactions contribution to a motor imagery classification task |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9798002/ https://www.ncbi.nlm.nih.gov/pubmed/36589279 http://dx.doi.org/10.3389/fncom.2022.990892 |
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