Warped phase coherence: An empirical synchronization measure combining phase and amplitude information.

The entrainment between weakly coupled nonlinear oscillators, as well as between complex signals such as those representing physiological activity, is frequently assessed in terms of whether a stable relationship is detectable between the instantaneous phases extracted from the measured or simulated time-series via the analytic signal. Here, we demonstrate that adding a possibly complex constant value to this normally null-mean signal has a non-trivial warping effect. Among other consequences, this introduces a level of sensitivity to the amplitude fluctuations and average relative phase. By means of simulations of Rössler systems and experiments on single-transistor oscillator networks, it is shown that the resulting coherence measure may have an empirical value in improving the inference of the structural couplings from the dynamics. When tentatively applied to the electroencephalogram recorded while performing imaginary and real movements, this straightforward modification of the phase locking value substantially improved the classification accuracy. Hence, its possible practical relevance in brain-computer and brain-machine interfaces deserves consideration.

[1]  Daniel Chicharro,et al.  Reliable detection of directional couplings using rank statistics. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  L. Faes,et al.  Assessment of Granger Causality by Nonlinear Model Identification: Application to Short-term Cardiovascular Variability , 2008, Annals of Biomedical Engineering.

[3]  Clemens Brunner,et al.  Online Control of a Brain-Computer Interface Using Phase Synchronization , 2006, IEEE Transactions on Biomedical Engineering.

[4]  Mattia Frasca,et al.  Amplitude dynamics favors synchronization in complex networks , 2016, Scientific Reports.

[5]  J. Kurths,et al.  Three types of transitions to phase synchronization in coupled chaotic oscillators. , 2003, Physical review letters.

[6]  Liam Paninski,et al.  Estimation of Entropy and Mutual Information , 2003, Neural Computation.

[7]  Ricardo Bruña,et al.  Phase locking value revisited: teaching new tricks to an old dog , 2017, Journal of neural engineering.

[8]  E. Ott Chaos in Dynamical Systems: Contents , 2002 .

[9]  Filippo Zappasodi,et al.  Impact of the reference choice on scalp EEG connectivity estimation , 2016, Journal of neural engineering.

[10]  J. Jovicich,et al.  Synchronization, non-linear dynamics and low-frequency fluctuations: analogy between spontaneous brain activity and networked single-transistor chaotic oscillators. , 2015, Chaos.

[11]  Cristina Masoller,et al.  Exact detection of direct links in networks of interacting dynamical units , 2014, 1403.4839.

[12]  Xiaoli Li,et al.  The comodulation measure of neuronal oscillations with general harmonic wavelet bicoherence and application to sleep analysis , 2009, NeuroImage.

[13]  Cristina Masoller,et al.  Inferring the connectivity of coupled oscillators from time-series statistical similarity analysis , 2015, Scientific Reports.

[14]  R. Ramírez-Mendoza,et al.  Motor imagery based brain–computer interfaces: An emerging technology to rehabilitate motor deficits , 2015, Neuropsychologia.

[15]  Louis M Pecora,et al.  Synchronization of chaotic systems. , 2015, Chaos.

[16]  Minkyu Ahn,et al.  Journal of Neuroscience Methods , 2015 .

[17]  Jürgen Kurths,et al.  Synchronization: Phase locking and frequency entrainment , 2001 .

[18]  Masa-aki Sato,et al.  Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns , 2008, NeuroImage.

[19]  Minyou Chen,et al.  EEG based zero-phase phase-locking value (PLV) and effects of spatial filtering during actual movement , 2017, Brain Research Bulletin.

[20]  N. Birbaumer,et al.  BCI2000: a general-purpose brain-computer interface (BCI) system , 2004, IEEE Transactions on Biomedical Engineering.

[21]  Ludovico Minati,et al.  Experimental dynamical characterization of five autonomous chaotic oscillators with tunable series resistance. , 2014, Chaos.

[22]  G. Pfurtscheller,et al.  Evidence for distinct beta resonance frequencies in human EEG related to specific sensorimotor cortical areas , 2001, Clinical Neurophysiology.

[23]  Konstantinos N. Plataniotis,et al.  Separable Common Spatio-Spectral Patterns for Motor Imagery BCI Systems , 2016, IEEE Transactions on Biomedical Engineering.

[24]  Schreiber,et al.  Improved Surrogate Data for Nonlinearity Tests. , 1996, Physical review letters.

[25]  Juan Garcia-Prieto,et al.  Efficient Computation of Functional Brain Networks: toward Real-Time Functional Connectivity , 2017, Front. Neuroinform..

[26]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[27]  Andreea Ioana Sburlea,et al.  Advantages of EEG phase patterns for the detection of gait intention in healthy and stroke subjects , 2016, Journal of neural engineering.

[28]  Kurths,et al.  Phase synchronization of chaotic oscillators. , 1996, Physical review letters.

[29]  J. Wolpaw,et al.  Mu and Beta Rhythm Topographies During Motor Imagery and Actual Movements , 2004, Brain Topography.

[30]  Christopher K. Kovach A Biased Look at Phase Locking: Brief Critical Review and Proposed Remedy , 2017, IEEE Transactions on Signal Processing.

[31]  O. Rössler An equation for continuous chaos , 1976 .

[32]  S. Boccaletti,et al.  Synchronization of chaotic systems , 2001 .

[33]  Boualem Boashash,et al.  Estimating and interpreting the instantaneous frequency of a signal. II. A/lgorithms and applications , 1992, Proc. IEEE.

[34]  M. Timme,et al.  Revealing networks from dynamics: an introduction , 2014, 1408.2963.

[35]  Joël M. H. Karel,et al.  Quantifying Neural Oscillatory Synchronization: A Comparison between Spectral Coherence and Phase-Locking Value Approaches , 2016, PloS one.

[36]  Yasuharu Koike,et al.  Decoding of Covert Vowel Articulation Using Electroencephalography Cortical Currents , 2016, Front. Neurosci..

[37]  S. Vijayan,et al.  The relationship between coherence and the phase-locking value. , 2017, Journal of theoretical biology.

[38]  R. Knight,et al.  The functional role of cross-frequency coupling , 2010, Trends in Cognitive Sciences.