Motor Imagery EEG Signals Decoding by Multivariate Empirical Wavelet Transform-Based Framework for Robust Brain–Computer Interfaces
The robustness and computational load are the key challenges in motor imagery (MI) based on electroencephalography (EEG) signals to decode for the development of practical brain-computer interface (BCI) systems. In this study, we propose a robust and simple automated multivariate empirical wavelet t...
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doaj-467cd6caec94443b9281320e51ec53c52021-03-30T00:49:18ZengIEEEIEEE Access2169-35362019-01-01717143117145110.1109/ACCESS.2019.29560188913546Motor Imagery EEG Signals Decoding by Multivariate Empirical Wavelet Transform-Based Framework for Robust Brain–Computer InterfacesMuhammad Tariq Sadiq0https://orcid.org/0000-0002-7410-5951Xiaojun Yu1https://orcid.org/0000-0001-7361-0780Zhaohui Yuan2https://orcid.org/0000-0002-2040-7815Fan Zeming3https://orcid.org/0000-0002-3422-9773Ateeq Ur Rehman4https://orcid.org/0000-0001-5203-0621Inam Ullah5https://orcid.org/0000-0002-5879-569XGuoqi Li6https://orcid.org/0000-0002-8994-431XGaoxi Xiao7https://orcid.org/0000-0002-4171-6799School of Automation, Northwestern Polytechnical University, Xi’an, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an, ChinaCollege of Internet of Things Engineering, Hohai University, Changzhou Campus, Changzhou, ChinaCollege of Internet of Things Engineering, Hohai University, Changzhou Campus, Changzhou, ChinaDepartment of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua University, Beijing, ChinaSchool of Electrical and Electronic Engineering, Nanyang Technological University, SingaporeThe robustness and computational load are the key challenges in motor imagery (MI) based on electroencephalography (EEG) signals to decode for the development of practical brain-computer interface (BCI) systems. In this study, we propose a robust and simple automated multivariate empirical wavelet transform (MEWT) algorithm for the decoding of different MI tasks. The main contributions of this study are four-fold. First, the multiscale principal component analysis method is utilized in the preprocessing module to obtain robustness against noise. Second, a novel automated channel selection strategy is proposed and then is further verified with comprehensive comparisons among three different strategies for decoding channel combination selection. Third, a sub-band alignment method by utilizing MEWT is adopted to obtain joint instantaneous amplitude and frequency components for the first time in MI applications. Four, a robust correlation-based feature selection strategy is applied to largely reduce the system complexity and computational load. Extensive experiments for subject-specific and subject independent cases are conducted with the three-benchmark datasets from BCI competition III to evaluate the performances of the proposed method by employing typical machine-learning classifiers. For subject-specific case, experimental results show that an average sensitivity, specificity and classification accuracy of 98% was achieved by employing multilayer perceptron neural networks, logistic model tree and least-square support vector machine (LS-SVM) classifiers, respectively for three datasets, resulting in an improvement of upto 23.50% in classification accuracy as compared with other existing method. While an average sensitivity, specificity and classification accuracy of 93%, 92.1% and 91.4% was achieved for subject independent case by employing LS-SVM classifier for all datasets with an increase of up to 18.14% relative to other existing methods. Results also show that our proposed algorithm provides a classification accuracy of 100% for subjects with small training size in subject-specific case, and for subject independent case by employing a single source subject. Such satisfactory results demonstrate the great potential of the proposed MEWT algorithm for practical MI EEG signals classification.https://ieeexplore.ieee.org/document/8913546/Electroencephalographymultiscale principal component analysisbrain-computer interfacemultivariate empirical wavelet transform |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Muhammad Tariq Sadiq Xiaojun Yu Zhaohui Yuan Fan Zeming Ateeq Ur Rehman Inam Ullah Guoqi Li Gaoxi Xiao |
spellingShingle |
Muhammad Tariq Sadiq Xiaojun Yu Zhaohui Yuan Fan Zeming Ateeq Ur Rehman Inam Ullah Guoqi Li Gaoxi Xiao Motor Imagery EEG Signals Decoding by Multivariate Empirical Wavelet Transform-Based Framework for Robust Brain–Computer Interfaces IEEE Access Electroencephalography multiscale principal component analysis brain-computer interface multivariate empirical wavelet transform |
author_facet |
Muhammad Tariq Sadiq Xiaojun Yu Zhaohui Yuan Fan Zeming Ateeq Ur Rehman Inam Ullah Guoqi Li Gaoxi Xiao |
author_sort |
Muhammad Tariq Sadiq |
title |
Motor Imagery EEG Signals Decoding by Multivariate Empirical Wavelet Transform-Based Framework for Robust Brain–Computer Interfaces |
title_short |
Motor Imagery EEG Signals Decoding by Multivariate Empirical Wavelet Transform-Based Framework for Robust Brain–Computer Interfaces |
title_full |
Motor Imagery EEG Signals Decoding by Multivariate Empirical Wavelet Transform-Based Framework for Robust Brain–Computer Interfaces |
title_fullStr |
Motor Imagery EEG Signals Decoding by Multivariate Empirical Wavelet Transform-Based Framework for Robust Brain–Computer Interfaces |
title_full_unstemmed |
Motor Imagery EEG Signals Decoding by Multivariate Empirical Wavelet Transform-Based Framework for Robust Brain–Computer Interfaces |
title_sort |
motor imagery eeg signals decoding by multivariate empirical wavelet transform-based framework for robust brain–computer interfaces |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
The robustness and computational load are the key challenges in motor imagery (MI) based on electroencephalography (EEG) signals to decode for the development of practical brain-computer interface (BCI) systems. In this study, we propose a robust and simple automated multivariate empirical wavelet transform (MEWT) algorithm for the decoding of different MI tasks. The main contributions of this study are four-fold. First, the multiscale principal component analysis method is utilized in the preprocessing module to obtain robustness against noise. Second, a novel automated channel selection strategy is proposed and then is further verified with comprehensive comparisons among three different strategies for decoding channel combination selection. Third, a sub-band alignment method by utilizing MEWT is adopted to obtain joint instantaneous amplitude and frequency components for the first time in MI applications. Four, a robust correlation-based feature selection strategy is applied to largely reduce the system complexity and computational load. Extensive experiments for subject-specific and subject independent cases are conducted with the three-benchmark datasets from BCI competition III to evaluate the performances of the proposed method by employing typical machine-learning classifiers. For subject-specific case, experimental results show that an average sensitivity, specificity and classification accuracy of 98% was achieved by employing multilayer perceptron neural networks, logistic model tree and least-square support vector machine (LS-SVM) classifiers, respectively for three datasets, resulting in an improvement of upto 23.50% in classification accuracy as compared with other existing method. While an average sensitivity, specificity and classification accuracy of 93%, 92.1% and 91.4% was achieved for subject independent case by employing LS-SVM classifier for all datasets with an increase of up to 18.14% relative to other existing methods. Results also show that our proposed algorithm provides a classification accuracy of 100% for subjects with small training size in subject-specific case, and for subject independent case by employing a single source subject. Such satisfactory results demonstrate the great potential of the proposed MEWT algorithm for practical MI EEG signals classification. |
topic |
Electroencephalography multiscale principal component analysis brain-computer interface multivariate empirical wavelet transform |
url |
https://ieeexplore.ieee.org/document/8913546/ |
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