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Realizing the Application of EEG Modeling in BCI Classification: Based on a Conditional GAN Converter

Electroencephalogram (EEG) modeling in brain-computer interface (BCI) provides a theoretical foundation for its development. However, limited by the lack of guidelines in model parameter selection and the inability to obtain personal tissue information in practice, EEG modeling in BCI is mainly focu...

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Autores principales: Zhang, Xiaodong, Lu, Zhufeng, Zhang, Teng, Li, Hanzhe, Wang, Yachun, Tao, Qing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8636039/
https://www.ncbi.nlm.nih.gov/pubmed/34867150
http://dx.doi.org/10.3389/fnins.2021.727394
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author Zhang, Xiaodong
Lu, Zhufeng
Zhang, Teng
Li, Hanzhe
Wang, Yachun
Tao, Qing
author_facet Zhang, Xiaodong
Lu, Zhufeng
Zhang, Teng
Li, Hanzhe
Wang, Yachun
Tao, Qing
author_sort Zhang, Xiaodong
collection PubMed
description Electroencephalogram (EEG) modeling in brain-computer interface (BCI) provides a theoretical foundation for its development. However, limited by the lack of guidelines in model parameter selection and the inability to obtain personal tissue information in practice, EEG modeling in BCI is mainly focused on the theoretical qualitative level which shows a gap between the theory and its application. Based on such problems, this work combined the surface EEG simulation with a converter based on the generative adversarial network (GAN), to establish the connection from simulated EEG to its application in BCI classification. For the scalp EEGs modeling, a mathematical model was built according to the physics of surface EEG, which consisted of the parallel 3-population neural mass model, the equivalent dipole, and the forward computation. For application, a converter based on the conditional GAN was designed, to transfer the simulated theoretical-only EEG to its practical version, in the lack of individual bio-information. To verify the feasibility, based on the latest microexpression-assisted BCI paradigm proposed by our group, the converted simulated EEGs were used in the training of BCI classifiers. The results indicated that, compared with training with insufficient real data, by adding the simulated EEGs, the overall performance showed a significant improvement (P = 0.04 < 0.05), and the test performance can be improved by 2.17% ± 4.23, in which the largest increase was up to 12.60% ± 1.81. Through this work, the link from theoretical EEG simulation to BCI classification has been initially established, providing an enhanced novel solution for the application of EEG modeling in BCI.
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spelling pubmed-86360392021-12-02 Realizing the Application of EEG Modeling in BCI Classification: Based on a Conditional GAN Converter Zhang, Xiaodong Lu, Zhufeng Zhang, Teng Li, Hanzhe Wang, Yachun Tao, Qing Front Neurosci Neuroscience Electroencephalogram (EEG) modeling in brain-computer interface (BCI) provides a theoretical foundation for its development. However, limited by the lack of guidelines in model parameter selection and the inability to obtain personal tissue information in practice, EEG modeling in BCI is mainly focused on the theoretical qualitative level which shows a gap between the theory and its application. Based on such problems, this work combined the surface EEG simulation with a converter based on the generative adversarial network (GAN), to establish the connection from simulated EEG to its application in BCI classification. For the scalp EEGs modeling, a mathematical model was built according to the physics of surface EEG, which consisted of the parallel 3-population neural mass model, the equivalent dipole, and the forward computation. For application, a converter based on the conditional GAN was designed, to transfer the simulated theoretical-only EEG to its practical version, in the lack of individual bio-information. To verify the feasibility, based on the latest microexpression-assisted BCI paradigm proposed by our group, the converted simulated EEGs were used in the training of BCI classifiers. The results indicated that, compared with training with insufficient real data, by adding the simulated EEGs, the overall performance showed a significant improvement (P = 0.04 < 0.05), and the test performance can be improved by 2.17% ± 4.23, in which the largest increase was up to 12.60% ± 1.81. Through this work, the link from theoretical EEG simulation to BCI classification has been initially established, providing an enhanced novel solution for the application of EEG modeling in BCI. Frontiers Media S.A. 2021-11-11 /pmc/articles/PMC8636039/ /pubmed/34867150 http://dx.doi.org/10.3389/fnins.2021.727394 Text en Copyright © 2021 Zhang, Lu, Zhang, Li, Wang and Tao. 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
Zhang, Xiaodong
Lu, Zhufeng
Zhang, Teng
Li, Hanzhe
Wang, Yachun
Tao, Qing
Realizing the Application of EEG Modeling in BCI Classification: Based on a Conditional GAN Converter
title Realizing the Application of EEG Modeling in BCI Classification: Based on a Conditional GAN Converter
title_full Realizing the Application of EEG Modeling in BCI Classification: Based on a Conditional GAN Converter
title_fullStr Realizing the Application of EEG Modeling in BCI Classification: Based on a Conditional GAN Converter
title_full_unstemmed Realizing the Application of EEG Modeling in BCI Classification: Based on a Conditional GAN Converter
title_short Realizing the Application of EEG Modeling in BCI Classification: Based on a Conditional GAN Converter
title_sort realizing the application of eeg modeling in bci classification: based on a conditional gan converter
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8636039/
https://www.ncbi.nlm.nih.gov/pubmed/34867150
http://dx.doi.org/10.3389/fnins.2021.727394
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