Quantification of Synchronization Processes by Coherence and Phase and Its Application in Analysis of Electrophysiological Signals

Abstract

Neuronal activity during information processing and muscle activity are generally characterized by oscillations. Mostly, widespread areas are involved and electrophysiological signals are measured on different sites of the cortex or of the muscle.In order to investigate functional relationships between different components of multidimensional electrophysiological signals, coherence and phase analyses turned out to be useful tools. These parameters allow the investigation of synchronization phenomena with regard to oscillations of defined frequencies or frequency bands.Coherence and phase are closely connected spectral parameters. Coherence may be understood as a measure of phase stability. Whereas coherence describes the amount of common information with regard to oscillations within certain frequency bands, the corresponding phase, from which time delays of these oscillations can be computed, hints at the direction of information transfer through oscillation.Coherence and phase analysis of surface EMG during continuous activity of deep and superficial muscles show distinct differences due to volume conduction properties of myoelectrical signals. Superficial activity therefore is characterized by significant coherence and stable phase relationships, which, additionally, can be used to determine motor unit action potential (MUAP) propagation velocity along the fibre direction without application of invasive methods. Deep muscle activity lacks significant coherence.Mental processes can be very brief and cooperation between different areas may be highly dynamic. For this reason in addition to usual Fourier estimation of coherence and phase, a two-dimensional approach of adaptive filtering was developed to estimate coherence and phase continuously in time. Statistical and dynamic properties of instantaneous phase are discussed.In order to demonstrate the value of this method for studying higher cognitive processes the method was applied to EEG recorded during word processing. During visual presentation of abstract nouns an information transfer through the propagation of oscillations from visual areas to frontal association areas in the α 1 -frequency band could be verified within the first 400 ms. In contrast, in case of auditory presentation positive phases from the temporal electrode locations T3 and T4 towards the occipital areas appear within the time interval of 300 ms-600 ms. The α 1 -band predominately seems to reflect sensory processing and attention processes.

EEG alpha power and coherence time courses in a sustained attention task

“…Coherence analysis has been used extensively to study the spatial and temporal structure of EEGs, particularly functional coupling between cortical structures and oscillatory information propagation during different tasks, e.g. finger movement [7] and word processing [8]. The results of the coherence study in [8] in particular supports previous findings that the alpha-1 band predominately reflects sensory processes and attention processes.…”

The contribution of EEG coherence to the investigation of language

“…This parametric procedure has the capacity to produce smooth, high-resolution spectra even from short data sets. The dynamic behavior of coherence and phase, calculated by means of a bivariate adaptive ARMA model estimation with time-varying parameters, was extensively examined for word processing (Rappelsberger, Weiss, & Schack, 1999;Schack, Rappelsberger, Anders, Weiss, & Moeller, 2000;Schack et al, 2001;Schack, Rappelsberger, Weiss, & Moeller, 1999b;Schack, Weiss, & Rappelsberger, 1996;Weiss, Mueller, & Rappelsberger, 1999) and other mental activities (e.g., Schack, Chen, Mescha, & Witte, 1999a;Schack & Krause, 1995).…”

Coherence

“…Coherence has been used in brain research to quantify the correlation in electrophysiologic activity between neighboring brain regions in studies related to dyslexia, epilepsy, sleep, cognition, and consciousness (1)(2)(3)(4)(5). Coherence has also received considerable attention in heart research as investigators have used the measure for automating arrhythmia detection (6,7), quantifying organization of fibrillation (8)(9)(10), and understanding the physiology underlying heart rate variability (11,12).…”