Synchronization Measures in EEG Signals



Synchronization phenomena have become an important feature in understanding the mechanism of normal or abnormal brain functions. As synchronization measures have their own principles to describe the EEG activities, it was difficult to give unified criteria for selecting effective synchronization methods to investigate different brain functions or understand mechanisms of different brain diseases. In this study, we gave an example of using different synchronization measures for tracking brain activity changes during propofol anesthesia. And the performance of these synchronization measures was evaluated in distinguishing the states of anesthesia.

We describe seven types of synchronization algorithms – including cross correlation (COR), cross coherence, phase synchronization (PS) mutual information based on kernel estimation (KerMI), permutation cross mutual information (PCMI), nonlinear interdependence (NI), and cross recurrence analysis. Models of Hénon, Rössler, and Lorenz systems were applied, and synchrony measures were tested to track changes of coupling strengths. Then, we analyzed real EEG data recorded from the prefrontal and primary motor areas of ten volunteers who undergone a brief standardized propofol anesthetic. To validate the relative effectiveness of these synchronization algorithms, we compared how well each algorithm modeled pharmacokinetic/pharmacodynamic (PK/PD) drug effects. We quantified the correlation coefficients (R ij ) between each synchrony measure and the bispectral index (BIS), the prediction probability (P k ) of each measure with the BIS and with effect-site propofol concentration (ESPC).

The synchronization measures, with the exception of WTC and determinism (DET), trended upward with increased coupling of the three studied models. The WTC and DET declined at low coupling strength and rose at high coupling strength. Propofol effects on brain activity could be tracked with reasonable accuracy by some of the synchronization measures. PCMI was the best method for discriminating the effect of anesthesia as measured by the correlation with BIS (\({R}_{ij}=0.84\)), prediction probability with BIS (\({P}_k=0.66\pm 0.08\)), and ESPC (\({P}_k=0.65\pm 0.07\)). PS based on phase-locking value (PSPLV), conditional probability (PSCP) in the \(\delta \left(1{-}4 Hz\right)\) frequency band, and Shannon entropy (PSSE) in the \(\beta \left(13{-}30 Hz\right)\) frequency band could distinguish the awake from unconsciousness state. KerMI, WTC, and DET measures increased with the increase of effect-site concentration. The cross correlation measure could not distinguish the different anesthesia states.

The synchronization measures investigated different aspects of the dynamics of coupled systems, reflecting the diverse intrinsic properties of the brain under anesthesia. Among the measures tested, the PCMI index had the best performance in tracking the effect of anesthesia and could be used as an index for monitoring the depth of anesthesia (DoA).

Synchronization measures are effective in estimating DoA based on EEG, with each measure exhibiting its own characteristic tendency. Of all the methods tested, the PCMI method has the greatest potential for tracking the effect of propofol on the brain activity.


Electroencephalogram Coupled model systems Synchronization measures Anesthesia Neurophysiological mechanisms Depth of anesthesia 


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Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.Institute of Electric EngineeringYanshan UniversityQinhuangdaoChina
  2. 2.Key Laboratory of Industrial Computer Control Engineering of Hebei ProvinceYanshan UniversityQinhuangdaoChina
  3. 3.State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
  4. 4.Center for Collaboration and Innovation in Brain and Learning SciencesBeijing Normal UniversityBeijingChina

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