Increasing the Temporal Resolution of Dynamic Functional Connectivity with Ensemble Empirical Mode Decomposition

[1]  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.

[2]  Karl J. Friston Functional and Effective Connectivity: A Review , 2011, Brain Connect..

[3]  C. Stam,et al.  Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources , 2007, Human brain mapping.

[4]  Yuko Mizuno-Matsumoto,et al.  Random Bin for Analyzing Neuron Spike Trains , 2012, Comput. Intell. Neurosci..

[5]  María Eugenia Torres,et al.  Improved complete ensemble EMD: A suitable tool for biomedical signal processing , 2014, Biomed. Signal Process. Control..

[6]  Gustavo Deco,et al.  Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? , 2016, NeuroImage.

[7]  David A. Leopold,et al.  Dynamic functional connectivity: Promise, issues, and interpretations , 2013, NeuroImage.

[8]  Patrick Flandrin,et al.  A complete ensemble empirical mode decomposition with adaptive noise , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[9]  Stephen M Smith,et al.  Correspondence of the brain's functional architecture during activation and rest , 2009, Proceedings of the National Academy of Sciences.

[10]  Mohammed Imamul Hassan Bhuiyan,et al.  Automatic sleep scoring using statistical features in the EMD domain and ensemble methods , 2016 .

[11]  Stephen M. Smith,et al.  Investigations into resting-state connectivity using independent component analysis , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[12]  Mark W. Woolrich,et al.  Measurement of dynamic task related functional networks using MEG , 2017, NeuroImage.

[13]  F. Varela,et al.  Measuring phase synchrony in brain signals , 1999, Human brain mapping.

[14]  S.J. Nasuto,et al.  Method for exploratory cluster analysis and visualisation of single-trial ERP ensembles , 2015, Journal of Neuroscience Methods.

[15]  M. Hallett,et al.  Identifying true brain interaction from EEG data using the imaginary part of coherency , 2004, Clinical Neurophysiology.

[16]  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.

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

[18]  V. Calhoun,et al.  EEG Signatures of Dynamic Functional Network Connectivity States , 2017, Brain Topography.

[19]  Thomas E. Nichols,et al.  Functional connectomics from resting-state fMRI , 2013, Trends in Cognitive Sciences.

[20]  Mirko van der Baan,et al.  Spectral estimation—What is new? What is next? , 2014 .

[21]  E. Foufoula‐Georgiou,et al.  Wavelet analysis for geophysical applications , 1997 .

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

[23]  G. Carter,et al.  Estimation of the magnitude-squared coherence function via overlapped fast Fourier transform processing , 1973 .

[24]  Norden E. Huang,et al.  Complementary Ensemble Empirical Mode Decomposition: a Novel Noise Enhanced Data Analysis Method , 2010, Adv. Data Sci. Adapt. Anal..

[25]  Dimitri Van De Ville,et al.  The dynamic functional connectome: State-of-the-art and perspectives , 2017, NeuroImage.