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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9370919 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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