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Subject-Independent EEG Classification of Motor Imagery Based on Dual-Branch Feature Fusion
A brain computer interface (BCI) system helps people with motor dysfunction interact with the external environment. With the advancement of technology, BCI systems have been applied in practice, but their practicability and usability are still greatly challenged. A large amount of calibration time i...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10377689/ https://www.ncbi.nlm.nih.gov/pubmed/37509039 http://dx.doi.org/10.3390/brainsci13071109 |
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author | Dong, Yanqing Wen, Xin Gao, Fang Gao, Chengxin Cao, Ruochen Xiang, Jie Cao, Rui |
author_facet | Dong, Yanqing Wen, Xin Gao, Fang Gao, Chengxin Cao, Ruochen Xiang, Jie Cao, Rui |
author_sort | Dong, Yanqing |
collection | PubMed |
description | A brain computer interface (BCI) system helps people with motor dysfunction interact with the external environment. With the advancement of technology, BCI systems have been applied in practice, but their practicability and usability are still greatly challenged. A large amount of calibration time is often required before BCI systems are used, which can consume the patient’s energy and easily lead to anxiety. This paper proposes a novel motion-assisted method based on a novel dual-branch multiscale auto encoder network (MSAENet) to decode human brain motion imagery intentions, while introducing a central loss function to compensate for the shortcomings of traditional classifiers that only consider inter-class differences and ignore intra-class coupling. The effectiveness of the method is validated on three datasets, namely BCIIV2a, SMR-BCI and OpenBMI, to achieve zero calibration of the MI-BCI system. The results show that our proposed network displays good results on all three datasets. In the case of subject-independence, the MSAENet outperformed the other four comparison methods on the BCIIV2a and SMR-BCI datasets, while achieving F1_score values as high as 69.34% on the OpenBMI dataset. Our method maintains better classification accuracy with a small number of parameters and short prediction times, and the method achieves zero calibration of the MI-BCI system. |
format | Online Article Text |
id | pubmed-10377689 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103776892023-07-29 Subject-Independent EEG Classification of Motor Imagery Based on Dual-Branch Feature Fusion Dong, Yanqing Wen, Xin Gao, Fang Gao, Chengxin Cao, Ruochen Xiang, Jie Cao, Rui Brain Sci Article A brain computer interface (BCI) system helps people with motor dysfunction interact with the external environment. With the advancement of technology, BCI systems have been applied in practice, but their practicability and usability are still greatly challenged. A large amount of calibration time is often required before BCI systems are used, which can consume the patient’s energy and easily lead to anxiety. This paper proposes a novel motion-assisted method based on a novel dual-branch multiscale auto encoder network (MSAENet) to decode human brain motion imagery intentions, while introducing a central loss function to compensate for the shortcomings of traditional classifiers that only consider inter-class differences and ignore intra-class coupling. The effectiveness of the method is validated on three datasets, namely BCIIV2a, SMR-BCI and OpenBMI, to achieve zero calibration of the MI-BCI system. The results show that our proposed network displays good results on all three datasets. In the case of subject-independence, the MSAENet outperformed the other four comparison methods on the BCIIV2a and SMR-BCI datasets, while achieving F1_score values as high as 69.34% on the OpenBMI dataset. Our method maintains better classification accuracy with a small number of parameters and short prediction times, and the method achieves zero calibration of the MI-BCI system. MDPI 2023-07-21 /pmc/articles/PMC10377689/ /pubmed/37509039 http://dx.doi.org/10.3390/brainsci13071109 Text en © 2023 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 Dong, Yanqing Wen, Xin Gao, Fang Gao, Chengxin Cao, Ruochen Xiang, Jie Cao, Rui Subject-Independent EEG Classification of Motor Imagery Based on Dual-Branch Feature Fusion |
title | Subject-Independent EEG Classification of Motor Imagery Based on Dual-Branch Feature Fusion |
title_full | Subject-Independent EEG Classification of Motor Imagery Based on Dual-Branch Feature Fusion |
title_fullStr | Subject-Independent EEG Classification of Motor Imagery Based on Dual-Branch Feature Fusion |
title_full_unstemmed | Subject-Independent EEG Classification of Motor Imagery Based on Dual-Branch Feature Fusion |
title_short | Subject-Independent EEG Classification of Motor Imagery Based on Dual-Branch Feature Fusion |
title_sort | subject-independent eeg classification of motor imagery based on dual-branch feature fusion |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10377689/ https://www.ncbi.nlm.nih.gov/pubmed/37509039 http://dx.doi.org/10.3390/brainsci13071109 |
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