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Cross-Modal Transfer Learning From EEG to Functional Near-Infrared Spectroscopy for Classification Task in Brain-Computer Interface System

The brain-computer interface (BCI) based on functional near-infrared spectroscopy (fNIRS) has received more and more attention due to its vast application potential in emotion recognition. However, the relatively insufficient investigation of the feature extraction algorithms limits its use in pract...

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Autores principales: Wang, Yuqing, Yang, Zhiqiang, Ji, Hongfei, Li, Jie, Liu, Lingyu, Zhuang, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9021696/
https://www.ncbi.nlm.nih.gov/pubmed/35465540
http://dx.doi.org/10.3389/fpsyg.2022.833007
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author Wang, Yuqing
Yang, Zhiqiang
Ji, Hongfei
Li, Jie
Liu, Lingyu
Zhuang, Jie
author_facet Wang, Yuqing
Yang, Zhiqiang
Ji, Hongfei
Li, Jie
Liu, Lingyu
Zhuang, Jie
author_sort Wang, Yuqing
collection PubMed
description The brain-computer interface (BCI) based on functional near-infrared spectroscopy (fNIRS) has received more and more attention due to its vast application potential in emotion recognition. However, the relatively insufficient investigation of the feature extraction algorithms limits its use in practice. In this article, to improve the performance of fNIRS-based BCI, we proposed a method named R-CSP-E, which introduces EEG signals when computing fNIRS signals’ features based on transfer learning and ensemble learning theory. In detail, we used the Independent Component Analysis (ICA) algorithm for the correspondence between the sources of the two signals. We then introduced the EEG signals when computing the spatial filter based on a modified Common Spatial Pattern (CSP) algorithm. Experimental results on public datasets show that the proposed method in this paper outperforms traditional methods without transfer. In general, the mean classification accuracy can be increased by up to 5%. To our knowledge, it is an innovation that we tried to apply transfer learning between EEG and fNIRS. Our study’s findings not only prove the potential of the transfer learning algorithm in cross-model brain-computer interface, but also offer a new and innovative perspective to research the hybrid brain-computer interface.
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spelling pubmed-90216962022-04-22 Cross-Modal Transfer Learning From EEG to Functional Near-Infrared Spectroscopy for Classification Task in Brain-Computer Interface System Wang, Yuqing Yang, Zhiqiang Ji, Hongfei Li, Jie Liu, Lingyu Zhuang, Jie Front Psychol Psychology The brain-computer interface (BCI) based on functional near-infrared spectroscopy (fNIRS) has received more and more attention due to its vast application potential in emotion recognition. However, the relatively insufficient investigation of the feature extraction algorithms limits its use in practice. In this article, to improve the performance of fNIRS-based BCI, we proposed a method named R-CSP-E, which introduces EEG signals when computing fNIRS signals’ features based on transfer learning and ensemble learning theory. In detail, we used the Independent Component Analysis (ICA) algorithm for the correspondence between the sources of the two signals. We then introduced the EEG signals when computing the spatial filter based on a modified Common Spatial Pattern (CSP) algorithm. Experimental results on public datasets show that the proposed method in this paper outperforms traditional methods without transfer. In general, the mean classification accuracy can be increased by up to 5%. To our knowledge, it is an innovation that we tried to apply transfer learning between EEG and fNIRS. Our study’s findings not only prove the potential of the transfer learning algorithm in cross-model brain-computer interface, but also offer a new and innovative perspective to research the hybrid brain-computer interface. Frontiers Media S.A. 2022-04-07 /pmc/articles/PMC9021696/ /pubmed/35465540 http://dx.doi.org/10.3389/fpsyg.2022.833007 Text en Copyright © 2022 Wang, Yang, Ji, Li, Liu and Zhuang. 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 Psychology
Wang, Yuqing
Yang, Zhiqiang
Ji, Hongfei
Li, Jie
Liu, Lingyu
Zhuang, Jie
Cross-Modal Transfer Learning From EEG to Functional Near-Infrared Spectroscopy for Classification Task in Brain-Computer Interface System
title Cross-Modal Transfer Learning From EEG to Functional Near-Infrared Spectroscopy for Classification Task in Brain-Computer Interface System
title_full Cross-Modal Transfer Learning From EEG to Functional Near-Infrared Spectroscopy for Classification Task in Brain-Computer Interface System
title_fullStr Cross-Modal Transfer Learning From EEG to Functional Near-Infrared Spectroscopy for Classification Task in Brain-Computer Interface System
title_full_unstemmed Cross-Modal Transfer Learning From EEG to Functional Near-Infrared Spectroscopy for Classification Task in Brain-Computer Interface System
title_short Cross-Modal Transfer Learning From EEG to Functional Near-Infrared Spectroscopy for Classification Task in Brain-Computer Interface System
title_sort cross-modal transfer learning from eeg to functional near-infrared spectroscopy for classification task in brain-computer interface system
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9021696/
https://www.ncbi.nlm.nih.gov/pubmed/35465540
http://dx.doi.org/10.3389/fpsyg.2022.833007
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