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Solving the SSVEP Paradigm Using the Nonlinear Canonical Correlation Analysis Approach

This paper presents the implementation of nonlinear canonical correlation analysis (NLCCA) approach to detect steady-state visual evoked potentials (SSVEP) quickly. The need for the fast recognition of proper stimulus to help end an SSVEP task in a BCI system is justified due to the flickering exter...

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Autores principales: De la Cruz-Guevara, Danni Rodrigo, Alfonso-Morales, Wilfredo, Caicedo-Bravo, Eduardo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8439358/
https://www.ncbi.nlm.nih.gov/pubmed/34450750
http://dx.doi.org/10.3390/s21165308
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author De la Cruz-Guevara, Danni Rodrigo
Alfonso-Morales, Wilfredo
Caicedo-Bravo, Eduardo
author_facet De la Cruz-Guevara, Danni Rodrigo
Alfonso-Morales, Wilfredo
Caicedo-Bravo, Eduardo
author_sort De la Cruz-Guevara, Danni Rodrigo
collection PubMed
description This paper presents the implementation of nonlinear canonical correlation analysis (NLCCA) approach to detect steady-state visual evoked potentials (SSVEP) quickly. The need for the fast recognition of proper stimulus to help end an SSVEP task in a BCI system is justified due to the flickering external stimulus exposure that causes users to start to feel fatigued. Measuring the accuracy and exposure time can be carried out through the information transfer rate—ITR, which is defined as a relationship between the precision, the number of stimuli, and the required time to obtain a result. NLCCA performance was evaluated by comparing it with two other approaches—the well-known canonical correlation analysis (CCA) and the least absolute reduction and selection operator (LASSO), both commonly used to solve the SSVEP paradigm. First, the best average ITR value was found from a dataset comprising ten healthy users with an average age of 28, where an exposure time of one second was obtained. In addition, the time sliding window responses were observed immediately after and around 200 ms after the flickering exposure to obtain the phase effects through the coefficient of variation (CV), where NLCCA obtained the lowest value. Finally, in order to obtain statistical significance to demonstrate that all approaches differ, the accuracy and ITR from the time sliding window responses was compared using a statistical analysis of variance per approach to identify differences between them using Tukey’s test.
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spelling pubmed-84393582021-09-15 Solving the SSVEP Paradigm Using the Nonlinear Canonical Correlation Analysis Approach De la Cruz-Guevara, Danni Rodrigo Alfonso-Morales, Wilfredo Caicedo-Bravo, Eduardo Sensors (Basel) Article This paper presents the implementation of nonlinear canonical correlation analysis (NLCCA) approach to detect steady-state visual evoked potentials (SSVEP) quickly. The need for the fast recognition of proper stimulus to help end an SSVEP task in a BCI system is justified due to the flickering external stimulus exposure that causes users to start to feel fatigued. Measuring the accuracy and exposure time can be carried out through the information transfer rate—ITR, which is defined as a relationship between the precision, the number of stimuli, and the required time to obtain a result. NLCCA performance was evaluated by comparing it with two other approaches—the well-known canonical correlation analysis (CCA) and the least absolute reduction and selection operator (LASSO), both commonly used to solve the SSVEP paradigm. First, the best average ITR value was found from a dataset comprising ten healthy users with an average age of 28, where an exposure time of one second was obtained. In addition, the time sliding window responses were observed immediately after and around 200 ms after the flickering exposure to obtain the phase effects through the coefficient of variation (CV), where NLCCA obtained the lowest value. Finally, in order to obtain statistical significance to demonstrate that all approaches differ, the accuracy and ITR from the time sliding window responses was compared using a statistical analysis of variance per approach to identify differences between them using Tukey’s test. MDPI 2021-08-06 /pmc/articles/PMC8439358/ /pubmed/34450750 http://dx.doi.org/10.3390/s21165308 Text en © 2021 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
De la Cruz-Guevara, Danni Rodrigo
Alfonso-Morales, Wilfredo
Caicedo-Bravo, Eduardo
Solving the SSVEP Paradigm Using the Nonlinear Canonical Correlation Analysis Approach
title Solving the SSVEP Paradigm Using the Nonlinear Canonical Correlation Analysis Approach
title_full Solving the SSVEP Paradigm Using the Nonlinear Canonical Correlation Analysis Approach
title_fullStr Solving the SSVEP Paradigm Using the Nonlinear Canonical Correlation Analysis Approach
title_full_unstemmed Solving the SSVEP Paradigm Using the Nonlinear Canonical Correlation Analysis Approach
title_short Solving the SSVEP Paradigm Using the Nonlinear Canonical Correlation Analysis Approach
title_sort solving the ssvep paradigm using the nonlinear canonical correlation analysis approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8439358/
https://www.ncbi.nlm.nih.gov/pubmed/34450750
http://dx.doi.org/10.3390/s21165308
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