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Classification of motor imagery electroencephalogram signals by using adaptive cross-subject transfer learning

INTRODUCTION: Electroencephalogram (EEG)-based motor imagery (MI) classification is an important aspect in brain-computer interfaces (BCIs), which bridges between neural system and computer devices decoding brain signals into recognizable machine commands. However, due to the small number of trainin...

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Autores principales: Feng, Jin, Li, Yunde, Jiang, Chengliang, Liu, Yu, Li, Mingxin, Hu, Qinghui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9811670/
https://www.ncbi.nlm.nih.gov/pubmed/36618992
http://dx.doi.org/10.3389/fnhum.2022.1068165
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author Feng, Jin
Li, Yunde
Jiang, Chengliang
Liu, Yu
Li, Mingxin
Hu, Qinghui
author_facet Feng, Jin
Li, Yunde
Jiang, Chengliang
Liu, Yu
Li, Mingxin
Hu, Qinghui
author_sort Feng, Jin
collection PubMed
description INTRODUCTION: Electroencephalogram (EEG)-based motor imagery (MI) classification is an important aspect in brain-computer interfaces (BCIs), which bridges between neural system and computer devices decoding brain signals into recognizable machine commands. However, due to the small number of training samples of MI electroencephalogram (MI-EEG) for a single subject and the great individual differences of MI-EEG among different subjects, the generalization and accuracy of the model on the specific MI task may be poor. METHODS: To solve these problems, an adaptive cross-subject transfer learning algorithm is proposed, which is based on kernel mean matching (KMM) and transfer learning adaptive boosting (TrAdaBoost) method. First, the common spatial pattern (CSP) is used to extract the spatial features. Then, in order to make the feature distribution more similar among different subjects, the KMM algorithm is used to compute a sample weight matrix for aligning the mean between source and target domains and reducing distribution differences among different subjects. Finally, the sample weight matrix from KMM is used as the initialization weight of TrAdaBoost, and then TrAdaBoost is used to adaptively select source domain samples that are closer to the target task distribution to assist in building a classification model. RESULTS: In order to verify the effectiveness and feasibility of the proposed method, the algorithm is applied to BCI Competition IV datasets and in-house datasets. The results show that the average classification accuracy of the proposed method on the public datasets is 89.1%, and the average classification accuracy on the in-house datasets is 80.4%. DISCUSSION: Compared with the existing methods, the proposed method effectively improves the classification accuracy of MI-EEG signals. At the same time, this paper also applies the proposed algorithm to the in-house dataset, the results verify the effectiveness of the algorithm again, and the results of this study have certain clinical guiding significance for brain rehabilitation.
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spelling pubmed-98116702023-01-05 Classification of motor imagery electroencephalogram signals by using adaptive cross-subject transfer learning Feng, Jin Li, Yunde Jiang, Chengliang Liu, Yu Li, Mingxin Hu, Qinghui Front Hum Neurosci Neuroscience INTRODUCTION: Electroencephalogram (EEG)-based motor imagery (MI) classification is an important aspect in brain-computer interfaces (BCIs), which bridges between neural system and computer devices decoding brain signals into recognizable machine commands. However, due to the small number of training samples of MI electroencephalogram (MI-EEG) for a single subject and the great individual differences of MI-EEG among different subjects, the generalization and accuracy of the model on the specific MI task may be poor. METHODS: To solve these problems, an adaptive cross-subject transfer learning algorithm is proposed, which is based on kernel mean matching (KMM) and transfer learning adaptive boosting (TrAdaBoost) method. First, the common spatial pattern (CSP) is used to extract the spatial features. Then, in order to make the feature distribution more similar among different subjects, the KMM algorithm is used to compute a sample weight matrix for aligning the mean between source and target domains and reducing distribution differences among different subjects. Finally, the sample weight matrix from KMM is used as the initialization weight of TrAdaBoost, and then TrAdaBoost is used to adaptively select source domain samples that are closer to the target task distribution to assist in building a classification model. RESULTS: In order to verify the effectiveness and feasibility of the proposed method, the algorithm is applied to BCI Competition IV datasets and in-house datasets. The results show that the average classification accuracy of the proposed method on the public datasets is 89.1%, and the average classification accuracy on the in-house datasets is 80.4%. DISCUSSION: Compared with the existing methods, the proposed method effectively improves the classification accuracy of MI-EEG signals. At the same time, this paper also applies the proposed algorithm to the in-house dataset, the results verify the effectiveness of the algorithm again, and the results of this study have certain clinical guiding significance for brain rehabilitation. Frontiers Media S.A. 2022-12-21 /pmc/articles/PMC9811670/ /pubmed/36618992 http://dx.doi.org/10.3389/fnhum.2022.1068165 Text en Copyright © 2022 Feng, Li, Jiang, Liu, Li and Hu. 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
Feng, Jin
Li, Yunde
Jiang, Chengliang
Liu, Yu
Li, Mingxin
Hu, Qinghui
Classification of motor imagery electroencephalogram signals by using adaptive cross-subject transfer learning
title Classification of motor imagery electroencephalogram signals by using adaptive cross-subject transfer learning
title_full Classification of motor imagery electroencephalogram signals by using adaptive cross-subject transfer learning
title_fullStr Classification of motor imagery electroencephalogram signals by using adaptive cross-subject transfer learning
title_full_unstemmed Classification of motor imagery electroencephalogram signals by using adaptive cross-subject transfer learning
title_short Classification of motor imagery electroencephalogram signals by using adaptive cross-subject transfer learning
title_sort classification of motor imagery electroencephalogram signals by using adaptive cross-subject transfer learning
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9811670/
https://www.ncbi.nlm.nih.gov/pubmed/36618992
http://dx.doi.org/10.3389/fnhum.2022.1068165
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