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Partial maximum correntropy regression for robust electrocorticography decoding
The Partial Least Square Regression (PLSR) method has shown admirable competence for predicting continuous variables from inter-correlated electrocorticography signals in the brain-computer interface. However, PLSR is essentially formulated with the least square criterion, thus, being considerably p...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10347400/ https://www.ncbi.nlm.nih.gov/pubmed/37457015 http://dx.doi.org/10.3389/fnins.2023.1213035 |
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author | Li, Yuanhao Chen, Badong Wang, Gang Yoshimura, Natsue Koike, Yasuharu |
author_facet | Li, Yuanhao Chen, Badong Wang, Gang Yoshimura, Natsue Koike, Yasuharu |
author_sort | Li, Yuanhao |
collection | PubMed |
description | The Partial Least Square Regression (PLSR) method has shown admirable competence for predicting continuous variables from inter-correlated electrocorticography signals in the brain-computer interface. However, PLSR is essentially formulated with the least square criterion, thus, being considerably prone to the performance deterioration caused by the brain recording noises. To address this problem, this study aims to propose a new robust variant for PLSR. To this end, the maximum correntropy criterion (MCC) is utilized to propose a new robust implementation of PLSR, called Partial Maximum Correntropy Regression (PMCR). The half-quadratic optimization is utilized to calculate the robust projectors for the dimensionality reduction, and the regression coefficients are optimized by a fixed-point optimization method. The proposed PMCR is evaluated with a synthetic example and a public electrocorticography dataset under three performance indicators. For the synthetic example, PMCR realized better prediction results compared with the other existing methods. PMCR could also abstract valid information with a limited number of decomposition factors in a noisy regression scenario. For the electrocorticography dataset, PMCR achieved superior decoding performance in most cases, and also realized the minimal neurophysiological pattern deterioration with the interference of the noises. The experimental results demonstrate that, the proposed PMCR could outperform the existing methods in a noisy, inter-correlated, and high-dimensional decoding task. PMCR could alleviate the performance degradation caused by the adverse noises and ameliorate the electrocorticography decoding robustness for the brain-computer interface. |
format | Online Article Text |
id | pubmed-10347400 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103474002023-07-15 Partial maximum correntropy regression for robust electrocorticography decoding Li, Yuanhao Chen, Badong Wang, Gang Yoshimura, Natsue Koike, Yasuharu Front Neurosci Neuroscience The Partial Least Square Regression (PLSR) method has shown admirable competence for predicting continuous variables from inter-correlated electrocorticography signals in the brain-computer interface. However, PLSR is essentially formulated with the least square criterion, thus, being considerably prone to the performance deterioration caused by the brain recording noises. To address this problem, this study aims to propose a new robust variant for PLSR. To this end, the maximum correntropy criterion (MCC) is utilized to propose a new robust implementation of PLSR, called Partial Maximum Correntropy Regression (PMCR). The half-quadratic optimization is utilized to calculate the robust projectors for the dimensionality reduction, and the regression coefficients are optimized by a fixed-point optimization method. The proposed PMCR is evaluated with a synthetic example and a public electrocorticography dataset under three performance indicators. For the synthetic example, PMCR realized better prediction results compared with the other existing methods. PMCR could also abstract valid information with a limited number of decomposition factors in a noisy regression scenario. For the electrocorticography dataset, PMCR achieved superior decoding performance in most cases, and also realized the minimal neurophysiological pattern deterioration with the interference of the noises. The experimental results demonstrate that, the proposed PMCR could outperform the existing methods in a noisy, inter-correlated, and high-dimensional decoding task. PMCR could alleviate the performance degradation caused by the adverse noises and ameliorate the electrocorticography decoding robustness for the brain-computer interface. Frontiers Media S.A. 2023-06-30 /pmc/articles/PMC10347400/ /pubmed/37457015 http://dx.doi.org/10.3389/fnins.2023.1213035 Text en Copyright © 2023 Li, Chen, Wang, Yoshimura and Koike. 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 Li, Yuanhao Chen, Badong Wang, Gang Yoshimura, Natsue Koike, Yasuharu Partial maximum correntropy regression for robust electrocorticography decoding |
title | Partial maximum correntropy regression for robust electrocorticography decoding |
title_full | Partial maximum correntropy regression for robust electrocorticography decoding |
title_fullStr | Partial maximum correntropy regression for robust electrocorticography decoding |
title_full_unstemmed | Partial maximum correntropy regression for robust electrocorticography decoding |
title_short | Partial maximum correntropy regression for robust electrocorticography decoding |
title_sort | partial maximum correntropy regression for robust electrocorticography decoding |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10347400/ https://www.ncbi.nlm.nih.gov/pubmed/37457015 http://dx.doi.org/10.3389/fnins.2023.1213035 |
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