MEG Data Analysis Using the Empirical Mode Decomposition Method

In the present paper, we propose to use the method of Empirical Mode Decomposition for frequency band analysis of MEG data. This method is compared with the more traditional methods of narrow band filtering and Hilbert transform. By the analysis of MEG data recorded during subjects’ volitional sensorimotor tasks, it is shown that the extraction of empirical modes can potentially detect some useful information about brain cognitive activity which is inaccessible to classical methods of frequency band analysis.

[1]  R. Ilmoniemi,et al.  Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain , 1993 .

[2]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[3]  J. Martinerie,et al.  Comparison of Hilbert transform and wavelet methods for the analysis of neuronal synchrony , 2001, Journal of Neuroscience Methods.

[4]  M. Pinsky.  Introduction to Fourier analysis and wavelets , 2002 .

[5]  A. Bruns.  Fourier-, Hilbert- and wavelet-based signal analysis: are they really different approaches? , 2004, Journal of Neuroscience Methods.

[6]  Paulo Gonçalves,et al.  Empirical Mode Decompositions as Data-Driven Wavelet-like Expansions , 2004, Int. J. Wavelets Multiresolution Inf. Process..

[7]  M. Kahana,et al.  Comparison of spectral analysis methods for characterizing brain oscillations , 2007, Journal of Neuroscience Methods.

[8]  Slawomir J. Nasuto,et al.  A novel approach to the detection of synchronisation in EEG based on empirical mode decomposition , 2007, Journal of Computational Neuroscience.

[9]  Stavros M. Panas,et al.  Hilbert-Huang Spectrum as a new field for the identification of EEG event related de-/synchronization for BCI applications , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  R. Fonseca-Pinto,et al.  On the influence of time-series length in EMD to extract frequency content: simulations and models in biomedical signals. , 2009, Medical engineering & physics.

[11]  Sabine Van Huffel,et al.  Source Separation From Single-Channel Recordings by Combining Empirical-Mode Decomposition and Independent Component Analysis , 2010, IEEE Transactions on Biomedical Engineering.

[12]  Robert Oostenveld,et al.  An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias , 2011, NeuroImage.

[13]  R. E. Greenblatt,et al.  Connectivity measures applied to human brain electrophysiological data , 2012, Journal of Neuroscience Methods.

[14]  Ram Bilas Pachori,et al.  Classification of Seizure and Nonseizure EEG Signals Using Empirical Mode Decomposition , 2012, IEEE Transactions on Information Technology in Biomedicine.

[15]  M. Lockwood,et al.  Extracting planetary waves from geomagnetic time series using Empirical Mode Decomposition , 2015 .

[16]  U. Rajendra Acharya,et al.  Application of empirical mode decomposition for analysis of normal and diabetic RR-interval signals , 2015, Expert Syst. Appl..