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Sparse representation of brain signals offers effective computation of cortico-muscular coupling value to predict the task-related and non-task sEMG channels: A joint hdEEG-sEMG study
Cortico-muscular interactions play important role in sensorimotor control during motor task and are commonly studied by cortico-muscular coherence (CMC) method using joint electroencephalogram-surface electromyogram (EEG-sEMG) signals. As noise and time delay between the two signals weaken the CMC v...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9249190/ https://www.ncbi.nlm.nih.gov/pubmed/35776772 http://dx.doi.org/10.1371/journal.pone.0270757 |
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author | Keihani, Ahmadreza Mohammadi, Amin Mohammad Marzbani, Hengameh Nafissi, Shahriar Haidari, Mohsen Reza Jafari, Amir Homayoun |
author_facet | Keihani, Ahmadreza Mohammadi, Amin Mohammad Marzbani, Hengameh Nafissi, Shahriar Haidari, Mohsen Reza Jafari, Amir Homayoun |
author_sort | Keihani, Ahmadreza |
collection | PubMed |
description | Cortico-muscular interactions play important role in sensorimotor control during motor task and are commonly studied by cortico-muscular coherence (CMC) method using joint electroencephalogram-surface electromyogram (EEG-sEMG) signals. As noise and time delay between the two signals weaken the CMC value, coupling difference between non-task sEMG channels is often undetectable. We used sparse representation of EEG channels to compute CMC and detect coupling for task-related and non-task sEMG signals. High-density joint EEG-sEMG (53 EEG channels, 4 sEMG bipolar channels) signals were acquired from 15 subjects (30.26 ± 4.96 years) during four specific hand and foot contraction tasks (2 dynamic and 2 static contraction). Sparse representations method was applied to detect projection of EEG signals on each sEMG channel. Bayesian optimization was employed to select best-fitted method with tuned hyperparameters on the input feeding data while using 80% data as the train set and 20% as test set. K-fold (K = 5) cross-validation method was used for evaluation of trained model. Two models were trained separately, one for CMC data and the other from sparse representation of EEG channels on each sEMG channel. Sensitivity, specificity, and accuracy criteria were obtained for test dataset to evaluate the performance of task-related and non-task sEMG channels detection. Coupling values were significantly different between grand average of task-related compared to the non-task sEMG channels (Z = -6.33, p< 0.001, task-related median = 2.011, non-task median = 0.112). Strong coupling index was found even in single trial analysis. Sparse representation approach (best fitted model: SVM, Accuracy = 88.12%, Sensitivity = 83.85%, Specificity = 92.45%) outperformed CMC method (best fitted model: KNN, Accuracy = 50.83%, Sensitivity = 52.17%, Specificity = 49.47%). Sparse representation approach offers high performance to detect CMC for discerning the EMG channels involved in the contraction tasks and non-tasks. |
format | Online Article Text |
id | pubmed-9249190 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-92491902022-07-02 Sparse representation of brain signals offers effective computation of cortico-muscular coupling value to predict the task-related and non-task sEMG channels: A joint hdEEG-sEMG study Keihani, Ahmadreza Mohammadi, Amin Mohammad Marzbani, Hengameh Nafissi, Shahriar Haidari, Mohsen Reza Jafari, Amir Homayoun PLoS One Research Article Cortico-muscular interactions play important role in sensorimotor control during motor task and are commonly studied by cortico-muscular coherence (CMC) method using joint electroencephalogram-surface electromyogram (EEG-sEMG) signals. As noise and time delay between the two signals weaken the CMC value, coupling difference between non-task sEMG channels is often undetectable. We used sparse representation of EEG channels to compute CMC and detect coupling for task-related and non-task sEMG signals. High-density joint EEG-sEMG (53 EEG channels, 4 sEMG bipolar channels) signals were acquired from 15 subjects (30.26 ± 4.96 years) during four specific hand and foot contraction tasks (2 dynamic and 2 static contraction). Sparse representations method was applied to detect projection of EEG signals on each sEMG channel. Bayesian optimization was employed to select best-fitted method with tuned hyperparameters on the input feeding data while using 80% data as the train set and 20% as test set. K-fold (K = 5) cross-validation method was used for evaluation of trained model. Two models were trained separately, one for CMC data and the other from sparse representation of EEG channels on each sEMG channel. Sensitivity, specificity, and accuracy criteria were obtained for test dataset to evaluate the performance of task-related and non-task sEMG channels detection. Coupling values were significantly different between grand average of task-related compared to the non-task sEMG channels (Z = -6.33, p< 0.001, task-related median = 2.011, non-task median = 0.112). Strong coupling index was found even in single trial analysis. Sparse representation approach (best fitted model: SVM, Accuracy = 88.12%, Sensitivity = 83.85%, Specificity = 92.45%) outperformed CMC method (best fitted model: KNN, Accuracy = 50.83%, Sensitivity = 52.17%, Specificity = 49.47%). Sparse representation approach offers high performance to detect CMC for discerning the EMG channels involved in the contraction tasks and non-tasks. Public Library of Science 2022-07-01 /pmc/articles/PMC9249190/ /pubmed/35776772 http://dx.doi.org/10.1371/journal.pone.0270757 Text en © 2022 Keihani et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Keihani, Ahmadreza Mohammadi, Amin Mohammad Marzbani, Hengameh Nafissi, Shahriar Haidari, Mohsen Reza Jafari, Amir Homayoun Sparse representation of brain signals offers effective computation of cortico-muscular coupling value to predict the task-related and non-task sEMG channels: A joint hdEEG-sEMG study |
title | Sparse representation of brain signals offers effective computation of cortico-muscular coupling value to predict the task-related and non-task sEMG channels: A joint hdEEG-sEMG study |
title_full | Sparse representation of brain signals offers effective computation of cortico-muscular coupling value to predict the task-related and non-task sEMG channels: A joint hdEEG-sEMG study |
title_fullStr | Sparse representation of brain signals offers effective computation of cortico-muscular coupling value to predict the task-related and non-task sEMG channels: A joint hdEEG-sEMG study |
title_full_unstemmed | Sparse representation of brain signals offers effective computation of cortico-muscular coupling value to predict the task-related and non-task sEMG channels: A joint hdEEG-sEMG study |
title_short | Sparse representation of brain signals offers effective computation of cortico-muscular coupling value to predict the task-related and non-task sEMG channels: A joint hdEEG-sEMG study |
title_sort | sparse representation of brain signals offers effective computation of cortico-muscular coupling value to predict the task-related and non-task semg channels: a joint hdeeg-semg study |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9249190/ https://www.ncbi.nlm.nih.gov/pubmed/35776772 http://dx.doi.org/10.1371/journal.pone.0270757 |
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