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Reducing calibration time in motor imagery-based BCIs by data alignment and empirical mode decomposition

One of the major reasons that limit the practical applications of a brain-computer interface (BCI) is its long calibration time. In this paper, we propose a novel approach to reducing the calibration time of motor imagery (MI)-based BCIs without sacrificing classification accuracy. The approach aims...

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Autores principales: Xiong, Wei, Wei, Qingguo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8824327/
https://www.ncbi.nlm.nih.gov/pubmed/35134085
http://dx.doi.org/10.1371/journal.pone.0263641
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author Xiong, Wei
Wei, Qingguo
author_facet Xiong, Wei
Wei, Qingguo
author_sort Xiong, Wei
collection PubMed
description One of the major reasons that limit the practical applications of a brain-computer interface (BCI) is its long calibration time. In this paper, we propose a novel approach to reducing the calibration time of motor imagery (MI)-based BCIs without sacrificing classification accuracy. The approach aims to augment the training set size of a new subject by generating artificial electroencephalogram (EEG) data from a few training trials initially available. The artificial EEG data are obtained by first performing empirical mode decomposition (EMD) and then mixing resulting intrinsic mode functions (IMFs). The original training trials are aligned to common reference point with Euclidean alignment (EA) method prior to EMD and pooled together with artificial trials as the expended training set, which is input into a linear discriminant analysis (LDA) classifier or a logistic regression (LR) classifier. The performance of the proposed algorithm is evaluated on two motor imagery (MI) data sets and compared with that of the algorithm trained with only real EEG data (Baseline) and the algorithm trained with expanded EEG data by EMD without data alignment. The experimental results showed that the proposed algorithm can significantly reduce the amount of training data needed to achieve a given performance level and thus is expected to facilitate the real-world applications of MI-based BCIs.
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spelling pubmed-88243272022-02-09 Reducing calibration time in motor imagery-based BCIs by data alignment and empirical mode decomposition Xiong, Wei Wei, Qingguo PLoS One Research Article One of the major reasons that limit the practical applications of a brain-computer interface (BCI) is its long calibration time. In this paper, we propose a novel approach to reducing the calibration time of motor imagery (MI)-based BCIs without sacrificing classification accuracy. The approach aims to augment the training set size of a new subject by generating artificial electroencephalogram (EEG) data from a few training trials initially available. The artificial EEG data are obtained by first performing empirical mode decomposition (EMD) and then mixing resulting intrinsic mode functions (IMFs). The original training trials are aligned to common reference point with Euclidean alignment (EA) method prior to EMD and pooled together with artificial trials as the expended training set, which is input into a linear discriminant analysis (LDA) classifier or a logistic regression (LR) classifier. The performance of the proposed algorithm is evaluated on two motor imagery (MI) data sets and compared with that of the algorithm trained with only real EEG data (Baseline) and the algorithm trained with expanded EEG data by EMD without data alignment. The experimental results showed that the proposed algorithm can significantly reduce the amount of training data needed to achieve a given performance level and thus is expected to facilitate the real-world applications of MI-based BCIs. Public Library of Science 2022-02-08 /pmc/articles/PMC8824327/ /pubmed/35134085 http://dx.doi.org/10.1371/journal.pone.0263641 Text en © 2022 Xiong, Wei 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
Xiong, Wei
Wei, Qingguo
Reducing calibration time in motor imagery-based BCIs by data alignment and empirical mode decomposition
title Reducing calibration time in motor imagery-based BCIs by data alignment and empirical mode decomposition
title_full Reducing calibration time in motor imagery-based BCIs by data alignment and empirical mode decomposition
title_fullStr Reducing calibration time in motor imagery-based BCIs by data alignment and empirical mode decomposition
title_full_unstemmed Reducing calibration time in motor imagery-based BCIs by data alignment and empirical mode decomposition
title_short Reducing calibration time in motor imagery-based BCIs by data alignment and empirical mode decomposition
title_sort reducing calibration time in motor imagery-based bcis by data alignment and empirical mode decomposition
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8824327/
https://www.ncbi.nlm.nih.gov/pubmed/35134085
http://dx.doi.org/10.1371/journal.pone.0263641
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