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EEG rhythm separation and time–frequency analysis of fast multivariate empirical mode decomposition for motor imagery BCI

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Abstract

Motor imagery electroencephalogram (EEG) is widely employed in brain–computer interface (BCI) systems. As a time–frequency analysis method for nonlinear and non-stationary signals, multivariate empirical mode decomposition (MEMD) and its noise-assisted version (NA-MEMD) has been widely used in the preprocessing step of BCI systems for separating EEG rhythms corresponding to specific brain activities. However, when applied to multichannel EEG signals, MEMD or NA-MEMD often demonstrate low robustness to noise and high computational complexity. To address these issues, we have explored the advantages of our recently proposed fast multivariate empirical mode decomposition (FMEMD) and its noise-assisted version (NA-FMEMD) for analyzing motor imagery data. We emphasize that FMEMD enables a more accurate estimation of EEG frequency information and exhibits a more noise-robust decomposition performance with improved computational efficiency. Comparative analysis with MEMD on simulation data and real-world EEG validates the above assertions. The joint average frequency measure is employed to automatically select intrinsic mode functions that correspond to specific frequency bands. Thus, FMEMD-based classification architecture is proposed. Using FMEMD as a preprocessing algorithm instead of MEMD can improve the classification accuracy by 2.3% on the BCI Competition IV dataset. On the Physiobank Motor/Mental Imagery dataset and BCI Competition IV Dataset 2a, FMEMD-based architecture also attained a comparable performance to complex algorithms. The results indicate that FMEMD proficiently extracts feature information from small benchmark datasets while mitigating dimensionality constraints resulting from computational complexity. Hence, FMEMD or NA-FMEMD can be a powerful time–frequency preprocessing method for BCI.

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Notes

  1. The mode mixing problem is defined as a signal IMF either consisting of signals of widely disparate scales, or a signal of a similar scale residing in different IMFs (Ur Rehman and Mandic 2011). The NA-MEMD method can effectively alleviate the mode mixing since it enforces the signal decomposition process based on the quasi-dyadic filter bank structure of MEMD.

  2. The implementations of LDA and SVM in the MATLAB Classification Learner App are used in this paper.

  3. The parameters of CSP filters are generated by only the training data sets.

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Acknowledgements

The authors wish to thank sponsor and financial support from National Natural Science Foundation of P.R. China (Grant Nos. 61134007, 61374121), the 111 Project (Grant No. B07031), and the Shenzhen Science and Technology Program (Grant No. KQTD20200820113106007).

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Authors and Affiliations

  1. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518026, China

    Yang Jiao, Qian Zheng & Yi Pan

  2. State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310027, China

    Dan Qiao & Lei Xie

  3. Department of Electronic Engineering, Information School, Yunnan University, Kunming, 650091, China

    Xun Lang

  4. University of Nottingham Ningbo China, Ningbo, 315100, China

    Yang Jiao

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Contributions

Yang Jiao and Qian Zheng wrote the main manuscript text and prepared most of figures. Yi pan provides our project funding. Lei Xie, Xun Lang and Dan Qiao assist in finishing table1 and table2. All authors reviewed the manuscript.

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Correspondence to Qian Zheng or Yi Pan.

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Communicated by Benjamin Lindner.

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Jiao, Y., Zheng, Q., Qiao, D. et al. EEG rhythm separation and time–frequency analysis of fast multivariate empirical mode decomposition for motor imagery BCI. Biol Cybern 118, 21–37 (2024). https://doi.org/10.1007/s00422-024-00984-1

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