Synchronization between brain signals can be quantified by mathematical approaches. Recent studies have proposed a large variety of synchrony methods to capture the synchrony in brain activity...Show moreSynchronization between brain signals can be quantified by mathematical approaches. Recent studies have proposed a large variety of synchrony methods to capture the synchrony in brain activity between interacting subjects. However, there is no detailed overview of how each synchrony method performs under different data characteristics. Here we investigate four synchrony methods, corr-entropy, S-estimator, Global field synchrony (GFS) and Omega complexity and this under varying data characteristics. These four synchrony methods are applied to time series simulated by two data generation mechanisms: Roessler system and linear multivariate autoregressive (MVAR) process. The simulated time series represent the brain activity of subjects and several data characteristics have been manipulated. The performance of each synchrony method is evaluated by root mean square error (RMSE) and the correlation coefficient between true and estimated synchrony values. Besides, the ANOVA analysis and effect sizes are introduced to test the influence of data characteristics on the performance of synchrony methods. The results show that the S-estimator is always the first or the second best performing method and corr-entropy outperforms other methods when it is applied to data generated by Roessler system. The coupling strength and the length of time series can interact with synchrony methods and significantly influence the performance of each method. Time series with high synchrony results in good performance of the S-estimator and poor performance of Corr-entropy. It turns out that the longer data length can lead to better performance of each synchrony method.Show less
Synchronization between brain signals can be quantifiedby mathematical approaches. Recent studies have proposed a large variety ofsynchrony methods tocapture the synchrony in brain activitybetween...Show moreSynchronization between brain signals can be quantifiedby mathematical approaches. Recent studies have proposed a large variety ofsynchrony methods tocapture the synchrony in brain activitybetween interacting subjects. However, there isno detailed overview ofhow each synchrony method performs under different data characteristics.Here we investigate four synchrony methods, corr-entropy, S-estimator, Globalfield synchrony (GFS) and Omega complexityand this under varying data characteristics. These four synchrony methods are applied to time series simulated by two data generation mechanisms: Roessler system and linear multivariate autoregressive (MVAR)process.The simulated time series represent the brain activity ofsubjects and several data characteristics have been manipulated.Theperformance of each synchrony method is evaluated by root mean square error (RMSE) and the correlation coefficient between true and estimated synchronyvalues. Besides, the ANOVA analysis and effect sizes are introduced to test the influence of data characteristicson the performance of synchrony methods.The results show that the S-estimator is always the first or the second best performing method and corr-entropy outperforms other methodswhen it is applied to data generated by Roessler system. Thecoupling strength and thelength of time series can interact with synchrony methodsand significantly influence the performanceof each method.Time series with high synchrony results in good performance of the S-estimator and poor performance of Corr-entropy. It turns out that the longer data length can lead to better performance of each synchrony method.Show less
Understanding the functioning of the brain and how it relates to behavior is one of theprimary objectives of neuroscience. The focus of neuroscience has evolved from a singlebrain to studying...Show moreUnderstanding the functioning of the brain and how it relates to behavior is one of theprimary objectives of neuroscience. The focus of neuroscience has evolved from a singlebrain to studying interactions between multiple brains. In several fields, synchrony inbrain responses between individuals has been proven to positively influence psychologicalprocesses and lead to better outcomes.Time-series data for each subject’s behavior or modality are obtained by measuringsynchrony. Comparative studies for synchrony methods have been carried out in orderto gain some insight into the similarities and differences between many measures forevaluating the synchrony between subjects using such time-series data. The research onlyprovides a partial picture of the performance of the synchrony methods in terms of captur-ing synchrony and the conditions in which these methods are optimal. It is still unknownhow well the synchrony methods perform when changing other data characteristics.The goal of this study is to evaluate the performance of several methods for capturingdifferent types of synchrony between a pair of time series. Two mechanisms are usedto generate a pair of time-series data with a known amount of synchrony between thetime series (1) two unidirectionally connected Hénon maps, and (2) bivariate von Misesdistribution. Correlations between the two time series are computed as another definitionof true synchrony to provide a different perspective on true synchrony. In addition, asystematical evaluation of the performance of the synchrony methods on simulated datawith various data characteristics is carried out.For the generated data coherence and phase synchrony are the two best performingmethods. Regarding the varied data characteristics, especially the amount of true syn-chrony has a large effect on recovery performance. These main effects between the datacharacteristics are qualified by several two-way and three-way interactions that almostalways include the synchrony methods and the amount of true synchrony. Under all ofthe different data characteristics, no synchrony method is perfect, and all of the synchronymethods in this study are not always stable. As a result, using a combination of differentsynchrony methods to detect synchrony is recommended.Show less
In neuroscience, recent research has seen the rise of complex network analysis methods to studybrain data from methods like EEG and fMRI. Among the many applications of complex networkanalysis, it...Show moreIn neuroscience, recent research has seen the rise of complex network analysis methods to studybrain data from methods like EEG and fMRI. Among the many applications of complex networkanalysis, it has been used to the study of interpersonal synchrony in brain data (M ̈uller andLindenberger, 2014; M ̈uller et al., 2013; S ̈anger et al., 2012). However, the use of complexnetwork analysis in this context is very limited, despite its high potential. To bridge this gap, inthis thesis, we present a simulation study allowing for a systematic comparison of the ability ofvarious network analysis measures to capture synchrony and this in the context of different inputparameters, functions to transform the data to be suited for network analysis and different typesof networks (topology). The results made clear that a lot of graph theory measures, mostlyfrom weighted and unweighted networks, were able to pick up on changes in synchrony. Theresults also showed the importance of lengthier EEG recordings, as that improved a lot theperformance of several graph theory measures. Moreover, the use of Pearson’s correlation andcircular correlation for transforming the data for network analysis appears to be a better choicethan using some other transform functions. Although further research must be done in the fieldof complex network measures for synchrony, the results are promising for the use of graph theorymeasures to detect changes in interpersonal synchrony.Show less
