EEG Mental Recognition Based on RKHS Learning and Source Dictionary Regularized RKHS Subspace Learning

This article mainly studies Electroencephalogram (EEG) mental recognition. Because the human brain is very complex and the EEG signal is greatly affected by the environment, EEG mental recognition can be attributed to domain adaptative problems. Our main work is as follows: (1) At present, most doma...

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Autores principales:	Wenjie Lei, Zhengming Ma, Shuyu Liu, Yuanping Lin
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Brain computer interface electroencephalogram domain adaptation learning subspace learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/233b466ea9994c6cb2c50f513e3d2de3
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id	oai:doaj.org-article:233b466ea9994c6cb2c50f513e3d2de3
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spelling	oai:doaj.org-article:233b466ea9994c6cb2c50f513e3d2de32021-11-18T00:08:38ZEEG Mental Recognition Based on RKHS Learning and Source Dictionary Regularized RKHS Subspace Learning2169-353610.1109/ACCESS.2021.3124028https://doaj.org/article/233b466ea9994c6cb2c50f513e3d2de32021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9592804/https://doaj.org/toc/2169-3536This article mainly studies Electroencephalogram (EEG) mental recognition. Because the human brain is very complex and the EEG signal is greatly affected by the environment, EEG mental recognition can be attributed to domain adaptative problems. Our main work is as follows: (1) At present, most domain adaptation learning only learns the linear subspace of Reproducing Kernel Hilbert Space (RKHS), and RKHS itself does not. Given the complexity and nonlinearity of EEG mental recognition, we propose an EEG mental recognition algorithm based on two learning. The two learning is RKHS learning and RKHS subspace learning. The source dictionary regularized RKHS subspace learning we proposed applies to EEG mental recognition and is better than pure RKHS subspace learning, but not enough. To get satisfactory results, we learn RKHS before RKHS subspace. (2) According to Moore–Aronszajn theorem, RKHS can be uniquely generated by a kernel function. The existing RKHS is rarely learnable. It is difficult to find a kernel function that can be learned and optimized. In RKHS learning, this paper uses a learnable kernel function that we published, and the kernel function is easy to optimize. (3) In RKHS subspace learning, most of the existing methods adopt the Maximum Mean Discrepancy (MMD) criterion, but it cannot make the spatial distribution of the same category of source and target domain data overlap as much as possible, and the label of the target domain data is unknown. To solve this problem, this paper proposes a framework of RKHS subspace learning based on source domain dictionary regularization. The experimental results on the brain-computer interface international competition data set (BCI competition IV 2a) show that the effect of the algorithm proposed in this paper is better than that of the other five advanced domain adaptation learning algorithms.Wenjie LeiZhengming MaShuyu LiuYuanping LinIEEEarticleBrain computer interfaceelectroencephalogramdomain adaptation learningsubspace learningElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 150545-150559 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Brain computer interface electroencephalogram domain adaptation learning subspace learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Brain computer interface electroencephalogram domain adaptation learning subspace learning Electrical engineering. Electronics. Nuclear engineering TK1-9971 Wenjie Lei Zhengming Ma Shuyu Liu Yuanping Lin EEG Mental Recognition Based on RKHS Learning and Source Dictionary Regularized RKHS Subspace Learning
description	This article mainly studies Electroencephalogram (EEG) mental recognition. Because the human brain is very complex and the EEG signal is greatly affected by the environment, EEG mental recognition can be attributed to domain adaptative problems. Our main work is as follows: (1) At present, most domain adaptation learning only learns the linear subspace of Reproducing Kernel Hilbert Space (RKHS), and RKHS itself does not. Given the complexity and nonlinearity of EEG mental recognition, we propose an EEG mental recognition algorithm based on two learning. The two learning is RKHS learning and RKHS subspace learning. The source dictionary regularized RKHS subspace learning we proposed applies to EEG mental recognition and is better than pure RKHS subspace learning, but not enough. To get satisfactory results, we learn RKHS before RKHS subspace. (2) According to Moore–Aronszajn theorem, RKHS can be uniquely generated by a kernel function. The existing RKHS is rarely learnable. It is difficult to find a kernel function that can be learned and optimized. In RKHS learning, this paper uses a learnable kernel function that we published, and the kernel function is easy to optimize. (3) In RKHS subspace learning, most of the existing methods adopt the Maximum Mean Discrepancy (MMD) criterion, but it cannot make the spatial distribution of the same category of source and target domain data overlap as much as possible, and the label of the target domain data is unknown. To solve this problem, this paper proposes a framework of RKHS subspace learning based on source domain dictionary regularization. The experimental results on the brain-computer interface international competition data set (BCI competition IV 2a) show that the effect of the algorithm proposed in this paper is better than that of the other five advanced domain adaptation learning algorithms.
format	article
author	Wenjie Lei Zhengming Ma Shuyu Liu Yuanping Lin
author_facet	Wenjie Lei Zhengming Ma Shuyu Liu Yuanping Lin
author_sort	Wenjie Lei
title	EEG Mental Recognition Based on RKHS Learning and Source Dictionary Regularized RKHS Subspace Learning
title_short	EEG Mental Recognition Based on RKHS Learning and Source Dictionary Regularized RKHS Subspace Learning
title_full	EEG Mental Recognition Based on RKHS Learning and Source Dictionary Regularized RKHS Subspace Learning
title_fullStr	EEG Mental Recognition Based on RKHS Learning and Source Dictionary Regularized RKHS Subspace Learning
title_full_unstemmed	EEG Mental Recognition Based on RKHS Learning and Source Dictionary Regularized RKHS Subspace Learning
title_sort	eeg mental recognition based on rkhs learning and source dictionary regularized rkhs subspace learning
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/233b466ea9994c6cb2c50f513e3d2de3
work_keys_str_mv	AT wenjielei eegmentalrecognitionbasedonrkhslearningandsourcedictionaryregularizedrkhssubspacelearning AT zhengmingma eegmentalrecognitionbasedonrkhslearningandsourcedictionaryregularizedrkhssubspacelearning AT shuyuliu eegmentalrecognitionbasedonrkhslearningandsourcedictionaryregularizedrkhssubspacelearning AT yuanpinglin eegmentalrecognitionbasedonrkhslearningandsourcedictionaryregularizedrkhssubspacelearning
_version_	1718425212554313728

EEG Mental Recognition Based on RKHS Learning and Source Dictionary Regularized RKHS Subspace Learning

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