Motor imagery EEG signals classification based on mode amplitude and frequency components using empirical wavelet transform

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

Research output: Contribution to journalArticlepeer-review


As one of the key techniques determining the overall system performances, efficient and reliable algorithms for improving the classification accuracy of motor imagery (MI) based electroencephalography (EEG) signals are highly desired for the development of brain-computer interface (BCI) systems. In this study, we propose, for the first time to the best of our knowledge, a novel data adaptive empirical wavelet transform (EWT) based signal decomposition method for improving the classification accuracy of MI based EEG signals. Specifically, to reduce the system complexity and execution time, the proposed method selects 18 electrodes out of 118 to analyze the non-stationary and nonlinear EEG signal behaviors. Meanwhile, the method adopts the Welch power spectral density (PSD) analysis method for single mode selection out of the total 10 for each channel, and the Hilbert transform (HT) method for both instantaneous amplitude (IA) and instantaneous frequency (IF) signal components extraction for each selected mode. With seven commonly used machine-learning classifiers adopted, extensive experiments were conducted with the benchmark dataset IVa from BCI competition III to evaluate the performance of the proposed method. Results show that with the IA and IF component features being tested using the least-square support vector machine (LS-SVM) classifier, the EWT method achieves an average classification accuracy of 95.2% and 94.6% respectively, which is higher as compared with the existing methods. While for every participant, a classification accuracy of at least 80% could be achieved by employing a single feature only. Results also show that a combination of EWT and higher order statistics features, which contain both kurtosis and skewness of the extracted instantaneous components, help achieve a higher success rate. The better performances of EWT over those of the existing methods demonstrate the effectiveness and great potential of EWT for BCI system applications.

Original languageEnglish
Article number8825825
Pages (from-to)127678-127692
Number of pages15
JournalIEEE Access
Publication statusPublished - 5 Sept 2019

Bibliographical note

Funding Information:
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, Northwestern Polytechnical University, under Grant ZZ2019028.


  • Brain-computer interface
  • electroencephalography
  • empirical wavelet transform
  • higher order statistics
  • motor imagery


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