Phase irregularity: A conceptually simple and efficient approach to characterize electroencephalographic recordings from epilepsy patients

Abstract

The severe neurological disorder epilepsy affects almost 1% of the world population. For patients who suffer from pharmacoresistant focal-onset epilepsy, electroencephalographic (EEG) recordings are essential for the localization of the brain area where seizures start. Apart from the visual inspection of the recordings, quantitative EEG signal analysis techniques proved to be useful for this purpose. Among other features, regularity versus irregularity and phase coherence versus phase independence allowed characterizing brain dynamics from the measured EEG signals. Can phase irregularities also characterize brain dynamics? To address this question, we use the univariate coefficient of phase velocity variation, defined as the ratio of phase velocity standard deviation and the mean phase velocity. Beyond that, as a bivariate measure we use the classical mean phase coherence to quantify the degree of phase locking. All phase-based measures are combined with surrogates to test null hypotheses about the dynamics underlying the signals. In the first part of our analysis, we use the Rössler model system to study our approach under controlled conditions. In the second part, we use the Bern-Barcelona EEG database which consists of focal and nonfocal signals extracted from seizure-free recordings. Focal signals are recorded from brain areas where the first seizure EEG signal changes can be detected, and nonfocal signals are recorded from areas that are not involved in the seizure at its onset. Our results show that focal signals have less phase variability and more phase coherence than nonfocal signals. Once combined with surrogates, the mean phase velocity proved to have the highest discriminative power between focal and nonfocal signals. In conclusion, conceptually simple and easy to compute phase-based measures can help to detect features induced by epilepsy from EEG signals. This holds not only for the classical mean phase coherence but even more so for univariate measures of phase irregularity.

Amplitude entropy to capture chimera-like behavior in the altered brain dynamics during seizures

“…In the current study, we also find a chimera-like network reorganization during seizure that manifests in an increased amplitude heterogeneity during seizure. Future work could aim to link this findings to other evidenced changes during the transition to seizure[34], [40]–[42], including changes in phase synchrony [9], [11]. Furthermore, it would be interesting to study a possible relation between the chimera-like behavior and the slow dynamics that govern fluctuations in seizure likelihood over time [41]–[43].…”

Network Analysis of Magnetoencephalogram Signals in Schizophrenia Patients When Viewing Emotional Facial Stimuli

“…This is because MEG has strong nonlinear properties, but the coherence describes the linear correlation in the frequency domain of the MEG signal, and can't explore the nonlinear correlation in the MEG signal [48], [65]. In addition, the phase lag index is sensitive to noise, and the noise signal in the MEG signal that is not processed cleanly will enhance the phase synchronization of the MEG signal leading to false connections [66].…”

Neurophysiological avenues to better conceptualizing adaptive cognition

“…The instantaneous phases can be used to characterize individual signals, pairs of signals, or multivariate sets of signals, thereby covering different spatial scales of neuronal organization. For individual signals, the degree of regularity versus irregularity can be quantified by the coefficient of phase velocity variation 43 . This approach allows one to assess the synchronization of local ensembles of neurons contributing to the signals measured at individual M/EEG sensors.…”