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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Xiaodong Zhang, Zhufeng Lu, Teng Zhang, Hanzhe Li, Yachun Wang, Qing Tao
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	BCI EEG GAN modeling simulation classification Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/8044c90a3f8642e69b806e9bd304e20d
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:8044c90a3f8642e69b806e9bd304e20d
record_format	dspace
spelling	oai:doaj.org-article:8044c90a3f8642e69b806e9bd304e20d2021-11-11T10:18:11ZRealizing the Application of EEG Modeling in BCI Classification: Based on a Conditional GAN Converter1662-453X10.3389/fnins.2021.727394https://doaj.org/article/8044c90a3f8642e69b806e9bd304e20d2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnins.2021.727394/fullhttps://doaj.org/toc/1662-453XElectroencephalogram (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.Xiaodong ZhangXiaodong ZhangZhufeng LuZhufeng LuTeng ZhangTeng ZhangHanzhe LiHanzhe LiYachun WangYachun WangQing TaoFrontiers Media S.A.articleBCIEEGGANmodelingsimulationclassificationNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Neuroscience, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	BCI EEG GAN modeling simulation classification Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	BCI EEG GAN modeling simulation classification Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Xiaodong Zhang Xiaodong Zhang Zhufeng Lu Zhufeng Lu Teng Zhang Teng Zhang Hanzhe Li Hanzhe Li Yachun Wang Yachun Wang Qing Tao Realizing the Application of EEG Modeling in BCI Classification: Based on a Conditional GAN Converter
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.
format	article
author	Xiaodong Zhang Xiaodong Zhang Zhufeng Lu Zhufeng Lu Teng Zhang Teng Zhang Hanzhe Li Hanzhe Li Yachun Wang Yachun Wang Qing Tao
author_facet	Xiaodong Zhang Xiaodong Zhang Zhufeng Lu Zhufeng Lu Teng Zhang Teng Zhang Hanzhe Li Hanzhe Li Yachun Wang Yachun Wang Qing Tao
author_sort	Xiaodong Zhang
title	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_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_sort	realizing the application of eeg modeling in bci classification: based on a conditional gan converter
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/8044c90a3f8642e69b806e9bd304e20d
work_keys_str_mv	AT xiaodongzhang realizingtheapplicationofeegmodelinginbciclassificationbasedonaconditionalganconverter AT xiaodongzhang realizingtheapplicationofeegmodelinginbciclassificationbasedonaconditionalganconverter AT zhufenglu realizingtheapplicationofeegmodelinginbciclassificationbasedonaconditionalganconverter AT zhufenglu realizingtheapplicationofeegmodelinginbciclassificationbasedonaconditionalganconverter AT tengzhang realizingtheapplicationofeegmodelinginbciclassificationbasedonaconditionalganconverter AT tengzhang realizingtheapplicationofeegmodelinginbciclassificationbasedonaconditionalganconverter AT hanzheli realizingtheapplicationofeegmodelinginbciclassificationbasedonaconditionalganconverter AT hanzheli realizingtheapplicationofeegmodelinginbciclassificationbasedonaconditionalganconverter AT yachunwang realizingtheapplicationofeegmodelinginbciclassificationbasedonaconditionalganconverter AT yachunwang realizingtheapplicationofeegmodelinginbciclassificationbasedonaconditionalganconverter AT qingtao realizingtheapplicationofeegmodelinginbciclassificationbasedonaconditionalganconverter
_version_	1718439231529943040

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

Ejemplares similares