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A Novel Quick-Response Eigenface Analysis Scheme for Brain–Computer Interfaces

The brain–computer interface (BCI) is used to understand brain activities and external bodies with the help of the motor imagery (MI). As of today, the classification results for EEG 4 class BCI competition dataset have been improved to provide better classification accuracy of the brain computer in...

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Autores principales: Choi, Hojong, Park, Junghun, Yang, Yeon-Mo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9370919/
https://www.ncbi.nlm.nih.gov/pubmed/35957420
http://dx.doi.org/10.3390/s22155860
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author Choi, Hojong
Park, Junghun
Yang, Yeon-Mo
author_facet Choi, Hojong
Park, Junghun
Yang, Yeon-Mo
author_sort Choi, Hojong
collection PubMed
description The brain–computer interface (BCI) is used to understand brain activities and external bodies with the help of the motor imagery (MI). As of today, the classification results for EEG 4 class BCI competition dataset have been improved to provide better classification accuracy of the brain computer interface systems (BCIs). Based on this observation, a novel quick-response eigenface analysis (QR-EFA) scheme for motor imagery is proposed to improve the classification accuracy for BCIs. Thus, we considered BCI signals in standardized and sharable quick response (QR) image domain; then, we systematically combined EFA and a convolution neural network (CNN) to classify the neuro images. To overcome a non-stationary BCI dataset available and non-ergodic characteristics, we utilized an effective neuro data augmentation in the training phase. For the ultimate improvements in classification performance, QR-EFA maximizes the similarities existing in the domain-, trial-, and subject-wise directions. To validate and verify the proposed scheme, we performed an experiment on the BCI dataset. Specifically, the scheme is intended to provide a higher classification output in classification accuracy performance for the BCI competition 4 dataset 2a (C4D2a_4C) and BCI competition 3 dataset 3a (C3D3a_4C). The experimental results confirm that the newly proposed QR-EFA method outperforms the previous the published results, specifically from 85.4% to 97.87% ± 0.75 for C4D2a_4C and 88.21% ± 6.02 for C3D3a_4C. Therefore, the proposed QR-EFA could be a highly reliable and constructive framework for one of the MI classification solutions for BCI applications.
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spelling pubmed-93709192022-08-12 A Novel Quick-Response Eigenface Analysis Scheme for Brain–Computer Interfaces Choi, Hojong Park, Junghun Yang, Yeon-Mo Sensors (Basel) Article The brain–computer interface (BCI) is used to understand brain activities and external bodies with the help of the motor imagery (MI). As of today, the classification results for EEG 4 class BCI competition dataset have been improved to provide better classification accuracy of the brain computer interface systems (BCIs). Based on this observation, a novel quick-response eigenface analysis (QR-EFA) scheme for motor imagery is proposed to improve the classification accuracy for BCIs. Thus, we considered BCI signals in standardized and sharable quick response (QR) image domain; then, we systematically combined EFA and a convolution neural network (CNN) to classify the neuro images. To overcome a non-stationary BCI dataset available and non-ergodic characteristics, we utilized an effective neuro data augmentation in the training phase. For the ultimate improvements in classification performance, QR-EFA maximizes the similarities existing in the domain-, trial-, and subject-wise directions. To validate and verify the proposed scheme, we performed an experiment on the BCI dataset. Specifically, the scheme is intended to provide a higher classification output in classification accuracy performance for the BCI competition 4 dataset 2a (C4D2a_4C) and BCI competition 3 dataset 3a (C3D3a_4C). The experimental results confirm that the newly proposed QR-EFA method outperforms the previous the published results, specifically from 85.4% to 97.87% ± 0.75 for C4D2a_4C and 88.21% ± 6.02 for C3D3a_4C. Therefore, the proposed QR-EFA could be a highly reliable and constructive framework for one of the MI classification solutions for BCI applications. MDPI 2022-08-05 /pmc/articles/PMC9370919/ /pubmed/35957420 http://dx.doi.org/10.3390/s22155860 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
Choi, Hojong
Park, Junghun
Yang, Yeon-Mo
A Novel Quick-Response Eigenface Analysis Scheme for Brain–Computer Interfaces
title A Novel Quick-Response Eigenface Analysis Scheme for Brain–Computer Interfaces
title_full A Novel Quick-Response Eigenface Analysis Scheme for Brain–Computer Interfaces
title_fullStr A Novel Quick-Response Eigenface Analysis Scheme for Brain–Computer Interfaces
title_full_unstemmed A Novel Quick-Response Eigenface Analysis Scheme for Brain–Computer Interfaces
title_short A Novel Quick-Response Eigenface Analysis Scheme for Brain–Computer Interfaces
title_sort novel quick-response eigenface analysis scheme for brain–computer interfaces
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9370919/
https://www.ncbi.nlm.nih.gov/pubmed/35957420
http://dx.doi.org/10.3390/s22155860
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