Abstract
Most EEG phase synchrony measures are of bivariate nature. Those that are multivariate focus on producing global indices of the synchronization state of the system. Thus, better descriptions of spatial and temporal local interactions are still in demand. A framework for characterization of phase synchrony relationships between multivariate neural time series is presented, applied either in a single epoch or over an intertrial assessment, relying on a proposed clustering algorithm, termed Multivariate Time Series Clustering by Phase Synchrony, which generates fuzzy clusters for each multivalued time sample and thereupon obtains hard clusters according to a circular variance threshold; such cluster modes are then depicted in Time-Frequency-Topography representations of synchrony state beyond mere global indices. EEG signals from P300 Speller sessions of four subjects were analyzed, obtaining useful insights of synchrony patterns related to the ERP and even revealing steady-state artifacts at 7.6 Hz. Further, contrast maps of Levenshtein Distance highlight synchrony differences between ERP and no-ERP epochs, mainly at delta and theta bands. The framework, which is not limited to one synchrony measure, allows observing dynamics of phase changes and interactions among channels and can be applied to analyze other cognitive states rather than ERP versus no ERP.
Highlights
There is a growing interest among the neuroscientific community to unravel the intricate neural mechanisms involved in the broad integration of different brain structures, which enable the emergence of cognitive processes
Remarkable to say, despite the lower signal-to-noise ratio (SNR) in a single trial compared to scalp map of Figure 5(d), Multivariate Time Series Clustering by Phase Synchrony (mCPS) is able to retrieve some of the electrodes within the blue cluster (Figure 5(c))
Several tests were made with different values of r, yet the results shown in this work are only for one r per subject, which was heuristically selected by identifying the TFL maps that yield a better differentiation of ERP and no-ERP conditions
Summary
There is a growing interest among the neuroscientific community to unravel the intricate neural mechanisms involved in the broad integration of different brain structures, which enable the emergence of cognitive processes. Several studies conducted with electroencephalography (EEG) and magnetoencephalography (MEG) have provided evidence that supports the idea of neural synchronization intrinsic to mental tasks, with the fluctuating disposition of communication channels in the nervous system, especially between active regions in the brain [1,2,3,4,5] In this regard, phase locking analysis of neural oscillations and other different measures of synchronization has gained attention, as several methods have been developed to provide a quantitative view of synchronism in brain sources and their behavior, estimating phase synchrony (PS) from different perspectives, depending on the purpose of the study in question [6]. For the sake of following a standard of terms, descriptions of any PS measure will follow the referred publication
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