Motor Imagery EEG Signals Decoding by Multivariate Empirical Wavelet Transform-Based Framework for Robust Brain–Computer Interfaces

Muhammad Tariq Sadiq, Xiaojun Yu, Zhaohui Yuan, Fan Zeming, Ateeq Ur Rehman, Inam Ullah, Guoqi Li, Gaoxi Xiao

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Article number8913546
Pages (from-to)171431-171451
Number of pages21
JournalIEEE Access
Publication statusPublished - 26 Nov 2019

Bibliographical note

Funding Information:
Corresponding authors: Xiaojun Yu ( and Zhaohui Yuan ( This work was supported in part by the National Natural Science Foundation of China under Grant 61705184 and Grant 51875477, in part by the Natural Science Basic Research Plan in Shaanxi Province of China under Grant 2018JQ6014, in part by the Fundamental Research Funds for the Central Universities under Grant G2018KY0308, in part by the China Postdoctoral Science Foundation under Grant 2018M641013, and in part by the Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University under Grant ZZ2019028.

Publisher Copyright:
© 2013 IEEE.


  • brain-computer interface
  • Electroencephalography
  • multiscale principal component analysis
  • multivariate empirical wavelet transform


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