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Motor Imagery Classification via Kernel-Based Domain Adaptation on an SPD Manifold

Background: Recording the calibration data of a brain–computer interface is a laborious process and is an unpleasant experience for the subjects. Domain adaptation is an effective technology to remedy the shortage of target data by leveraging rich labeled data from the sources. However, most prior m...

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Autores principales: Jiang, Qin, Zhang, Yi, Zheng, Kai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9139384/
https://www.ncbi.nlm.nih.gov/pubmed/35625045
http://dx.doi.org/10.3390/brainsci12050659
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author Jiang, Qin
Zhang, Yi
Zheng, Kai
author_facet Jiang, Qin
Zhang, Yi
Zheng, Kai
author_sort Jiang, Qin
collection PubMed
description Background: Recording the calibration data of a brain–computer interface is a laborious process and is an unpleasant experience for the subjects. Domain adaptation is an effective technology to remedy the shortage of target data by leveraging rich labeled data from the sources. However, most prior methods have needed to extract the features of the EEG signal first, which triggers another challenge in BCI classification, due to small sample sets or a lack of labels for the target. Methods: In this paper, we propose a novel domain adaptation framework, referred to as kernel-based Riemannian manifold domain adaptation (KMDA). KMDA circumvents the tedious feature extraction process by analyzing the covariance matrices of electroencephalogram (EEG) signals. Covariance matrices define a symmetric positive definite space (SPD) that can be described by Riemannian metrics. In KMDA, the covariance matrices are aligned in the Riemannian manifold, and then are mapped to a high dimensional space by a log-Euclidean metric Gaussian kernel, where subspace learning is performed by minimizing the conditional distribution distance between the sources and the target while preserving the target discriminative information. We also present an approach to convert the EEG trials into 2D frames (E-frames) to further lower the dimension of covariance descriptors. Results: Experiments on three EEG datasets demonstrated that KMDA outperforms several state-of-the-art domain adaptation methods in classification accuracy, with an average Kappa of 0.56 for BCI competition IV dataset IIa, 0.75 for BCI competition IV dataset IIIa, and an average accuracy of 81.56% for BCI competition III dataset IVa. Additionally, the overall accuracy was further improved by 5.28% with the E-frames. KMDA showed potential in addressing subject dependence and shortening the calibration time of motor imagery-based brain–computer interfaces.
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spelling pubmed-91393842022-05-28 Motor Imagery Classification via Kernel-Based Domain Adaptation on an SPD Manifold Jiang, Qin Zhang, Yi Zheng, Kai Brain Sci Article Background: Recording the calibration data of a brain–computer interface is a laborious process and is an unpleasant experience for the subjects. Domain adaptation is an effective technology to remedy the shortage of target data by leveraging rich labeled data from the sources. However, most prior methods have needed to extract the features of the EEG signal first, which triggers another challenge in BCI classification, due to small sample sets or a lack of labels for the target. Methods: In this paper, we propose a novel domain adaptation framework, referred to as kernel-based Riemannian manifold domain adaptation (KMDA). KMDA circumvents the tedious feature extraction process by analyzing the covariance matrices of electroencephalogram (EEG) signals. Covariance matrices define a symmetric positive definite space (SPD) that can be described by Riemannian metrics. In KMDA, the covariance matrices are aligned in the Riemannian manifold, and then are mapped to a high dimensional space by a log-Euclidean metric Gaussian kernel, where subspace learning is performed by minimizing the conditional distribution distance between the sources and the target while preserving the target discriminative information. We also present an approach to convert the EEG trials into 2D frames (E-frames) to further lower the dimension of covariance descriptors. Results: Experiments on three EEG datasets demonstrated that KMDA outperforms several state-of-the-art domain adaptation methods in classification accuracy, with an average Kappa of 0.56 for BCI competition IV dataset IIa, 0.75 for BCI competition IV dataset IIIa, and an average accuracy of 81.56% for BCI competition III dataset IVa. Additionally, the overall accuracy was further improved by 5.28% with the E-frames. KMDA showed potential in addressing subject dependence and shortening the calibration time of motor imagery-based brain–computer interfaces. MDPI 2022-05-18 /pmc/articles/PMC9139384/ /pubmed/35625045 http://dx.doi.org/10.3390/brainsci12050659 Text en © 2022 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
Jiang, Qin
Zhang, Yi
Zheng, Kai
Motor Imagery Classification via Kernel-Based Domain Adaptation on an SPD Manifold
title Motor Imagery Classification via Kernel-Based Domain Adaptation on an SPD Manifold
title_full Motor Imagery Classification via Kernel-Based Domain Adaptation on an SPD Manifold
title_fullStr Motor Imagery Classification via Kernel-Based Domain Adaptation on an SPD Manifold
title_full_unstemmed Motor Imagery Classification via Kernel-Based Domain Adaptation on an SPD Manifold
title_short Motor Imagery Classification via Kernel-Based Domain Adaptation on an SPD Manifold
title_sort motor imagery classification via kernel-based domain adaptation on an spd manifold
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9139384/
https://www.ncbi.nlm.nih.gov/pubmed/35625045
http://dx.doi.org/10.3390/brainsci12050659
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AT zhengkai motorimageryclassificationviakernelbaseddomainadaptationonanspdmanifold