EMDLAB: A toolbox for analysis of single-trial EEG dynamics using empirical mode decomposition

BACKGROUND Empirical mode decomposition (EMD) is an empirical data decomposition technique. Recently there is growing interest in applying EMD in the biomedical field. NEW METHOD EMDLAB is an extensible plug-in for the EEGLAB toolbox, which is an open software environment for electrophysiological data analysis. RESULTS EMDLAB can be used to perform, easily and effectively, four common types of EMD: plain EMD, ensemble EMD (EEMD), weighted sliding EMD (wSEMD) and multivariate EMD (MEMD) on EEG data. In addition, EMDLAB is a user-friendly toolbox and closely implemented in the EEGLAB toolbox. COMPARISON WITH EXISTING METHODS EMDLAB gains an advantage over other open-source toolboxes by exploiting the advantageous visualization capabilities of EEGLAB for extracted intrinsic mode functions (IMFs) and Event-Related Modes (ERMs) of the signal. CONCLUSIONS EMDLAB is a reliable, efficient, and automated solution for extracting and visualizing the extracted IMFs and ERMs by EMD algorithms in EEG study.

[1]  Norden E. Huang,et al.  A review on Hilbert‐Huang transform: Method and its applications to geophysical studies , 2008 .

[2]  Wen-Liang Hwang,et al.  EMD Revisited: A New Understanding of the Envelope and Resolving the Mode-Mixing Problem in AM-FM Signals , 2012, IEEE Transactions on Signal Processing.

[3]  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.

[4]  Karl J. Friston,et al.  Academic Software Applications for Electromagnetic Brain Mapping Using MEG and EEG , 2011, Comput. Intell. Neurosci..

[5]  Andrzej Cichocki,et al.  Emd Approach to Multichannel EEG Data - the amplitude and Phase Components Clustering Analysis , 2010, J. Circuits Syst. Comput..

[6]  S. Quek,et al.  Comparison of Hilbert- Huang, Wavelet, and Fourier Transforms for Selected Applications , 2005 .

[7]  D. P. Mandic,et al.  Multivariate empirical mode decomposition , 2010, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[8]  Elmar Wolfgang Lang,et al.  Sliding Empirical Mode Decomposition , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[9]  Fabian J. Theis,et al.  Exploratory Matrix Factorization Techniques for Large Scale Biomedical Data Sets , 2011 .

[10]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[11]  S. Dalal,et al.  Prestimulus Oscillatory Phase at 7 Hz Gates Cortical Information Flow and Visual Perception , 2013, Current Biology.

[12]  Ali Yener Mutlu,et al.  Multivariate Empirical Mode Decomposition for Quantifying Multivariate Phase Synchronization , 2011, EURASIP J. Adv. Signal Process..

[13]  Scott Makeig,et al.  BCILAB: a platform for brain–computer interface development , 2013, Journal of neural engineering.

[14]  Shawn W. Ell,et al.  The neurobiology of human category learning , 2001, Trends in Cognitive Sciences.

[15]  Arnaud Delorme,et al.  EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing , 2011, Comput. Intell. Neurosci..

[16]  Arthur W. Toga,et al.  Construction of a 3D probabilistic atlas of human cortical structures , 2008, NeuroImage.

[17]  Scott Makeig,et al.  Neuroelectromagnetic Forward Head Modeling Toolbox , 2010, Journal of Neuroscience Methods.

[18]  Guo Xiao-jing,et al.  The EEG Signal Preprocessing Based on Empirical Mode Decomposition , 2008, 2008 2nd International Conference on Bioinformatics and Biomedical Engineering.

[19]  Danilo P. Mandic,et al.  Emd via mEMD: multivariate noise-Aided Computation of Standard EMD , 2013, Adv. Data Sci. Adapt. Anal..

[20]  Elmar Wolfgang Lang,et al.  Weighted sliding Empirical Mode Decomposition , 2011, Adv. Data Sci. Adapt. Anal..

[21]  Danilo P. Mandic,et al.  Empirical Mode Decomposition-Based Time-Frequency Analysis of Multivariate Signals: The Power of Adaptive Data Analysis , 2013, IEEE Signal Processing Magazine.

[22]  N. Huang,et al.  A study of the characteristics of white noise using the empirical mode decomposition method , 2004, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[23]  Xiangming Kong,et al.  Error analysis and implementation considerations of decoding algorithms for time-encoding machine , 2011, EURASIP J. Adv. Signal Process..

[24]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[25]  Gabriel Rilling,et al.  Empirical mode decomposition as a filter bank , 2004, IEEE Signal Processing Letters.

[26]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[27]  Allan Kardec Barros,et al.  Auditory Feedback for Brain Computer Interface Management - An EEG Data Sonification Approach , 2006, KES.

[28]  S. Makeig,et al.  Mining event-related brain dynamics , 2004, Trends in Cognitive Sciences.

[29]  K. Blinowska,et al.  Multichannel matching pursuit and EEG inverse solutions , 2005, Journal of Neuroscience Methods.

[30]  S. Rétaux,et al.  Evolutionary Shift from Fighting to Foraging in Blind Cavefish through Changes in the Serotonin Network , 2013, Current Biology.

[31]  Hee-Seok Oh,et al.  EMD: A Package for Empirical Mode Decomposition and Hilbert Spectrum , 2009 .

[32]  Danilo P. Mandic,et al.  Filter Bank Property of Multivariate Empirical Mode Decomposition , 2011, IEEE Transactions on Signal Processing.

[33]  David Looney,et al.  Intrinsic multi-scale analysis: a multi-variate empirical mode decomposition framework , 2015, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[34]  Rainer Hammwöhner,et al.  Ensemble Empirical Mode Decomposition Analysis of EEG Data Collected during a Contour Integration Task , 2015, PloS one.

[35]  Gabriel Rilling,et al.  On empirical mode decomposition and its algorithms , 2003 .

[36]  Rainer Hammwöhner,et al.  Bidimensional ensemble empirical mode decomposition of functional biomedical images taken during a contour integration task , 2014, Biomed. Signal Process. Control..

[37]  Juan Manuel Górriz,et al.  Brain Connectivity Analysis: A Short Survey , 2012, Comput. Intell. Neurosci..

[38]  Nii O. Attoh-Okine,et al.  The Empirical Mode Decomposition and the Hilbert-Huang Transform , 2008, EURASIP J. Adv. Signal Process..

[39]  Jr. S. Marple,et al.  Computing the discrete-time 'analytic' signal via FFT , 1999, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).

[40]  S. Calabro,et al.  Advances in signal processing , 2012, 2012 38th European Conference and Exhibition on Optical Communications.

[41]  Elmar Wolfgang Lang,et al.  Weighted Sliding Empirical Mode Decomposition for Online Analysis of Biomedical Time Series , 2012, Neural Processing Letters.