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Data Augmentation: Using Channel-Level Recombination to Improve Classification Performance for Motor Imagery EEG

The quality and quantity of training data are crucial to the performance of a deep-learning-based brain-computer interface (BCI) system. However, it is not practical to record EEG data over several long calibration sessions. A promising time- and cost-efficient solution is artificial data generation...

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Autores principales: Pei, Yu, Luo, Zhiguo, Yan, Ye, Yan, Huijiong, Jiang, Jing, Li, Weiguo, Xie, Liang, Yin, Erwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7990774/
https://www.ncbi.nlm.nih.gov/pubmed/33776673
http://dx.doi.org/10.3389/fnhum.2021.645952
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author Pei, Yu
Luo, Zhiguo
Yan, Ye
Yan, Huijiong
Jiang, Jing
Li, Weiguo
Xie, Liang
Yin, Erwei
author_facet Pei, Yu
Luo, Zhiguo
Yan, Ye
Yan, Huijiong
Jiang, Jing
Li, Weiguo
Xie, Liang
Yin, Erwei
author_sort Pei, Yu
collection PubMed
description The quality and quantity of training data are crucial to the performance of a deep-learning-based brain-computer interface (BCI) system. However, it is not practical to record EEG data over several long calibration sessions. A promising time- and cost-efficient solution is artificial data generation or data augmentation (DA). Here, we proposed a DA method for the motor imagery (MI) EEG signal called brain-area-recombination (BAR). For the BAR, each sample was first separated into two ones (named half-sample) by left/right brain channels, and the artificial samples were generated by recombining the half-samples. We then designed two schemas (intra- and adaptive-subject schema) corresponding to the single- and multi-subject scenarios. Extensive experiments using the classifier of EEGnet were conducted on two public datasets under various training set sizes. In both schemas, the BAR method can make the EEGnet have a better performance of classification (p < 0.01). To make a comparative investigation, we selected two common DA methods (noise-added and flipping), and the BAR method beat them (p < 0.05). Further, using the proposed BAR for augmentation, EEGnet achieved up to 8.3% improvement than a typical decoding algorithm CSP-SVM (p < 0.01), note that both the models were trained on the augmented dataset. This study shows that BAR usage can significantly improve the classification ability of deep learning to MI-EEG signals. To a certain extent, it may promote the development of deep learning technology in the field of BCI.
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spelling pubmed-79907742021-03-26 Data Augmentation: Using Channel-Level Recombination to Improve Classification Performance for Motor Imagery EEG Pei, Yu Luo, Zhiguo Yan, Ye Yan, Huijiong Jiang, Jing Li, Weiguo Xie, Liang Yin, Erwei Front Hum Neurosci Human Neuroscience The quality and quantity of training data are crucial to the performance of a deep-learning-based brain-computer interface (BCI) system. However, it is not practical to record EEG data over several long calibration sessions. A promising time- and cost-efficient solution is artificial data generation or data augmentation (DA). Here, we proposed a DA method for the motor imagery (MI) EEG signal called brain-area-recombination (BAR). For the BAR, each sample was first separated into two ones (named half-sample) by left/right brain channels, and the artificial samples were generated by recombining the half-samples. We then designed two schemas (intra- and adaptive-subject schema) corresponding to the single- and multi-subject scenarios. Extensive experiments using the classifier of EEGnet were conducted on two public datasets under various training set sizes. In both schemas, the BAR method can make the EEGnet have a better performance of classification (p < 0.01). To make a comparative investigation, we selected two common DA methods (noise-added and flipping), and the BAR method beat them (p < 0.05). Further, using the proposed BAR for augmentation, EEGnet achieved up to 8.3% improvement than a typical decoding algorithm CSP-SVM (p < 0.01), note that both the models were trained on the augmented dataset. This study shows that BAR usage can significantly improve the classification ability of deep learning to MI-EEG signals. To a certain extent, it may promote the development of deep learning technology in the field of BCI. Frontiers Media S.A. 2021-03-11 /pmc/articles/PMC7990774/ /pubmed/33776673 http://dx.doi.org/10.3389/fnhum.2021.645952 Text en Copyright © 2021 Pei, Luo, Yan, Yan, Jiang, Li, Xie and Yin. http://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 Human Neuroscience
Pei, Yu
Luo, Zhiguo
Yan, Ye
Yan, Huijiong
Jiang, Jing
Li, Weiguo
Xie, Liang
Yin, Erwei
Data Augmentation: Using Channel-Level Recombination to Improve Classification Performance for Motor Imagery EEG
title Data Augmentation: Using Channel-Level Recombination to Improve Classification Performance for Motor Imagery EEG
title_full Data Augmentation: Using Channel-Level Recombination to Improve Classification Performance for Motor Imagery EEG
title_fullStr Data Augmentation: Using Channel-Level Recombination to Improve Classification Performance for Motor Imagery EEG
title_full_unstemmed Data Augmentation: Using Channel-Level Recombination to Improve Classification Performance for Motor Imagery EEG
title_short Data Augmentation: Using Channel-Level Recombination to Improve Classification Performance for Motor Imagery EEG
title_sort data augmentation: using channel-level recombination to improve classification performance for motor imagery eeg
topic Human Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7990774/
https://www.ncbi.nlm.nih.gov/pubmed/33776673
http://dx.doi.org/10.3389/fnhum.2021.645952
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