Quantifying mode mixing and leakage in multivariate empirical mode decomposition and application in motor imagery–based brain-computer interface system
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Improper selection of the number and the amplitude of noise channels in noise-assisted multivariate empirical mode decomposition (NA-MEMD) would induce mode mixing and leakage in the obtained intrinsic mode functions (IMF), which would degrade the performance in applications like brain-computer interface (BCI) systems based on motor imagery. A measurement (ML-index) using no prior knowledge of the underlying components of the original signals was proposed to quantify the amount of mode mixing and leakage of IMFs. Both synthetic signals and electroencephalography (EEG) recordings from motor imagery experiments were used to test the validity. The BCI classification performance using NA-MEMD with the optimal parameters selected based on the ML-index was compared with the performance under the non-optimal parameter condition and the performance using the conventional filtering method. Test on synthetic signals demonstrated the ML-index can effectively quantify the amount of mode mixing and leakage, and help to improve the accuracy of extracting the underlying components. Test on EEG recordings showed the BCI classification performance can be significantly improved under the optimal parameter condition. This study provided a method to quantify the amount of mode mixing and leakage in IMFs and realized the optimization of the parameters associated with noise channels in NA-MEMD.
KeywordsMultivariate empirical mode decomposition Mode mixing BCI Motor imagery Intrinsic mode functions
This work was supported by the project funded by China Postdoctoral Science Foundation (Grant No. 2017M620445) and National Natural Science Foundation of China (Grant No. 51475360).
Compliance with ethical standards
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Conflict of interest
The authors declare that they have no conflict of interest.
- 6.McCane LM, Heckman SM, McFarland DJ, Townsend G, Mak JN, Sellers EW, Zeitlin D, Tenteromano LM, Wolpaw JR, Vaughan TM (2015) P300-based brain-computer interface (BCI) event-related potentials (ERPs): people with amyotrophic lateral sclerosis (ALS) vs. age-matched controls. Clin Neurophysiol 126:2124–2131CrossRefGoogle Scholar
- 8.Gao L, Wang J, Li J, Zheng Y Design of BCI based multi-information system to restore hand motor function for stroke patients. In: Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on, 2013. IEEE, pp 4924–4928Google Scholar
- 16.Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen N-C, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In: Proceedings of the Royal Society of London A: mathematical, physical and engineering sciences, . The Royal Society, pp 903–995, 454Google Scholar
- 18.Huang L, Huang X, Wang Y-T, Wang Y, Jung T-P, Cheng C-K (2013).Empirical mode decomposition improves detection of SSVEP. In: Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, IEEE, pp 3901–3904Google Scholar
- 20.Looney D, Park C, Kidmose P, Ungstrup M, Mandic D (2009) Measuring phase synchrony using complex extensions of EMD. In: Statistical Signal Processing, SSP'09. IEEE/SP 15th Workshop on, 2009. IEEE, pp 49–52Google Scholar
- 23.Rilling G, Flandrin P, Fellow, Gonçalves P, Lilly JM (2007) Bivariate empirical mode decomposition. IEEE SIGNAL PROCESSING LETTERS:10Google Scholar
- 25.Rehman N, Mandic DP Multivariate empirical mode decomposition.(2009) In: Proceedings of The Royal Society of London A: Mathematical, Physical and Engineering Sciences The Royal Society, p rspa20090502Google Scholar
- 29.Wu Z, Huang NE (2004) A study of the characteristics of white noise using the empirical mode decomposition method. In: Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences,. vol 2046. The Royal Society, pp 1597–1611, 460Google Scholar