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Decoding of Turning Intention during Walking Based on EEG Biomarkers

In the EEG literature, there is a lack of asynchronous intention models that realistically propose interfaces for applications that must operate in real time. In this work, a novel BMI approach to detect in real time the intention to turn is proposed. For this purpose, an offline, pseudo-online and...

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Autores principales: Quiles, Vicente, Ferrero, Laura, Iáñez, Eduardo, Ortiz, Mario, Azorín, José M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9330787/
https://www.ncbi.nlm.nih.gov/pubmed/35892452
http://dx.doi.org/10.3390/bios12080555
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author Quiles, Vicente
Ferrero, Laura
Iáñez, Eduardo
Ortiz, Mario
Azorín, José M.
author_facet Quiles, Vicente
Ferrero, Laura
Iáñez, Eduardo
Ortiz, Mario
Azorín, José M.
author_sort Quiles, Vicente
collection PubMed
description In the EEG literature, there is a lack of asynchronous intention models that realistically propose interfaces for applications that must operate in real time. In this work, a novel BMI approach to detect in real time the intention to turn is proposed. For this purpose, an offline, pseudo-online and online analysis is presented to validate the EEG as a biomarker for the intention to turn. This article presents a methodology for the creation of a BMI that could differentiate two classes: monotonous walk and intention to turn. A comparison of some of the most popular algorithms in the literature is conducted. To filter the signal, two relevant algorithms are used: [Formula: see text] filter and ASR. For processing and classification, the mean of the covariance matrices in the Riemannian space was calculated and then, with various classifiers of different types, the distance of the test samples to each class in the Riemannian space was estimated. This dispenses with power-based models and the necessary baseline correction, which is a problem in realistic scenarios. In the cross-validation for a generic selection (valid for any subject) and a personalized one, the results were, on average, 66.2% and 69.6% with the best filter [Formula: see text]. For the pseudo-online, the custom configuration for each subject was an average of 40.2% TP and 9.3 FP/min; the best subject obtained 43.9% TP and 2.9 FP/min. In the final validation test, this subject obtained 2.5 FP/min and an accuracy rate of 71.43%, and the turn anticipation was 0.21 s on average.
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spelling pubmed-93307872022-07-29 Decoding of Turning Intention during Walking Based on EEG Biomarkers Quiles, Vicente Ferrero, Laura Iáñez, Eduardo Ortiz, Mario Azorín, José M. Biosensors (Basel) Article In the EEG literature, there is a lack of asynchronous intention models that realistically propose interfaces for applications that must operate in real time. In this work, a novel BMI approach to detect in real time the intention to turn is proposed. For this purpose, an offline, pseudo-online and online analysis is presented to validate the EEG as a biomarker for the intention to turn. This article presents a methodology for the creation of a BMI that could differentiate two classes: monotonous walk and intention to turn. A comparison of some of the most popular algorithms in the literature is conducted. To filter the signal, two relevant algorithms are used: [Formula: see text] filter and ASR. For processing and classification, the mean of the covariance matrices in the Riemannian space was calculated and then, with various classifiers of different types, the distance of the test samples to each class in the Riemannian space was estimated. This dispenses with power-based models and the necessary baseline correction, which is a problem in realistic scenarios. In the cross-validation for a generic selection (valid for any subject) and a personalized one, the results were, on average, 66.2% and 69.6% with the best filter [Formula: see text]. For the pseudo-online, the custom configuration for each subject was an average of 40.2% TP and 9.3 FP/min; the best subject obtained 43.9% TP and 2.9 FP/min. In the final validation test, this subject obtained 2.5 FP/min and an accuracy rate of 71.43%, and the turn anticipation was 0.21 s on average. MDPI 2022-07-22 /pmc/articles/PMC9330787/ /pubmed/35892452 http://dx.doi.org/10.3390/bios12080555 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Quiles, Vicente
Ferrero, Laura
Iáñez, Eduardo
Ortiz, Mario
Azorín, José M.
Decoding of Turning Intention during Walking Based on EEG Biomarkers
title Decoding of Turning Intention during Walking Based on EEG Biomarkers
title_full Decoding of Turning Intention during Walking Based on EEG Biomarkers
title_fullStr Decoding of Turning Intention during Walking Based on EEG Biomarkers
title_full_unstemmed Decoding of Turning Intention during Walking Based on EEG Biomarkers
title_short Decoding of Turning Intention during Walking Based on EEG Biomarkers
title_sort decoding of turning intention during walking based on eeg biomarkers
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9330787/
https://www.ncbi.nlm.nih.gov/pubmed/35892452
http://dx.doi.org/10.3390/bios12080555
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