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Deep Neural Regression Prediction of Motor Imagery Skills Using EEG Functional Connectivity Indicators

Motor imaging (MI) induces recovery and neuroplasticity in neurophysical regulation. However, a non-negligible portion of users presents insufficient coordination skills of sensorimotor cortex control. Assessments of the relationship between wakefulness and tasks states are conducted to foster neuro...

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Autores principales: Caicedo-Acosta, Julian, Castaño, German A., Acosta-Medina, Carlos, Alvarez-Meza, Andres, Castellanos-Dominguez, German
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7999933/
https://www.ncbi.nlm.nih.gov/pubmed/33801817
http://dx.doi.org/10.3390/s21061932
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author Caicedo-Acosta, Julian
Castaño, German A.
Acosta-Medina, Carlos
Alvarez-Meza, Andres
Castellanos-Dominguez, German
author_facet Caicedo-Acosta, Julian
Castaño, German A.
Acosta-Medina, Carlos
Alvarez-Meza, Andres
Castellanos-Dominguez, German
author_sort Caicedo-Acosta, Julian
collection PubMed
description Motor imaging (MI) induces recovery and neuroplasticity in neurophysical regulation. However, a non-negligible portion of users presents insufficient coordination skills of sensorimotor cortex control. Assessments of the relationship between wakefulness and tasks states are conducted to foster neurophysiological and mechanistic interpretation in MI-related applications. Thus, to understand the organization of information processing, measures of functional connectivity are used. Also, models of neural network regression prediction are becoming popular, These intend to reduce the need for extracting features manually. However, predicting MI practicing’s neurophysiological inefficiency raises several problems, like enhancing network regression performance because of the overfitting risk. Here, to increase the prediction performance, we develop a deep network regression model that includes three procedures: leave-one-out cross-validation combined with Monte Carlo dropout layers, subject clustering of MI inefficiency, and transfer learning between neighboring runs. Validation is performed using functional connectivity predictors extracted from two electroencephalographic databases acquired in conditions close to real MI applications (150 users), resulting in a high prediction of pretraining desynchronization and initial training synchronization with adequate physiological interpretability.
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spelling pubmed-79999332021-03-28 Deep Neural Regression Prediction of Motor Imagery Skills Using EEG Functional Connectivity Indicators Caicedo-Acosta, Julian Castaño, German A. Acosta-Medina, Carlos Alvarez-Meza, Andres Castellanos-Dominguez, German Sensors (Basel) Article Motor imaging (MI) induces recovery and neuroplasticity in neurophysical regulation. However, a non-negligible portion of users presents insufficient coordination skills of sensorimotor cortex control. Assessments of the relationship between wakefulness and tasks states are conducted to foster neurophysiological and mechanistic interpretation in MI-related applications. Thus, to understand the organization of information processing, measures of functional connectivity are used. Also, models of neural network regression prediction are becoming popular, These intend to reduce the need for extracting features manually. However, predicting MI practicing’s neurophysiological inefficiency raises several problems, like enhancing network regression performance because of the overfitting risk. Here, to increase the prediction performance, we develop a deep network regression model that includes three procedures: leave-one-out cross-validation combined with Monte Carlo dropout layers, subject clustering of MI inefficiency, and transfer learning between neighboring runs. Validation is performed using functional connectivity predictors extracted from two electroencephalographic databases acquired in conditions close to real MI applications (150 users), resulting in a high prediction of pretraining desynchronization and initial training synchronization with adequate physiological interpretability. MDPI 2021-03-10 /pmc/articles/PMC7999933/ /pubmed/33801817 http://dx.doi.org/10.3390/s21061932 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Caicedo-Acosta, Julian
Castaño, German A.
Acosta-Medina, Carlos
Alvarez-Meza, Andres
Castellanos-Dominguez, German
Deep Neural Regression Prediction of Motor Imagery Skills Using EEG Functional Connectivity Indicators
title Deep Neural Regression Prediction of Motor Imagery Skills Using EEG Functional Connectivity Indicators
title_full Deep Neural Regression Prediction of Motor Imagery Skills Using EEG Functional Connectivity Indicators
title_fullStr Deep Neural Regression Prediction of Motor Imagery Skills Using EEG Functional Connectivity Indicators
title_full_unstemmed Deep Neural Regression Prediction of Motor Imagery Skills Using EEG Functional Connectivity Indicators
title_short Deep Neural Regression Prediction of Motor Imagery Skills Using EEG Functional Connectivity Indicators
title_sort deep neural regression prediction of motor imagery skills using eeg functional connectivity indicators
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7999933/
https://www.ncbi.nlm.nih.gov/pubmed/33801817
http://dx.doi.org/10.3390/s21061932
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