EEG-Chimera-Project - Nonlinear Time Series Analysis (UPF)

Our group is currently running the research project "Chimera states and electroencephalographic (EEG) recordings from epilepsy patients: a combined analysis". We are grateful for the funding the Spanish Ministry of Science and Innovation and the State Research Agency has provided for this project (Grant No. PID2020-118196GBI00/MICIN/AEI/10.13039/501100011033).

Below please find our first publications derived from this project. For each publication, you will find a brief summary and link to the printed article. Furthermore, each article has a news release directed to the public. We provide short videos summarizing the work for the non-expert and expert audience. To allow othe scientists to build on our results, we provide links to the computer source code underlying our studies.

As a further entry you will find information on the "Research in 4 minutes" competition which our project team member Anaïs Espinoso won in May 2023 by presenting her work, which is embedded in our project.

What is out project about?

Our project is positioned at the interface between physics, applied mathematics, neuroscience and neurology. The overarching objective is to narrow the gap between studies of mathematical models of networks in so-called chimera states on the one hand, and real neuronal dynamics affected by epilepsy on the other hand. Our working hypothesis is that similar mechanisms of synchronization and desynchronization can be found for both dynamics. We will therefore perform a combined analysis of chimera state networks and intracranial electroencephalographic recordings (EEG) from epilepsy patients. We thereby build a bridge between basic research on chimera states and applied research on epilepsy.

Research in 4 minutes: "Phase studies to analyse brain signals from epilepsy patients" (2023)

With the following presentation, Anaïs Espinoso was proclaimed the winner of the 2023 edition of Rin4’ (Research in Four Minutes). It is a scientific outreach competition, organized by the UPF Doctoral School since 2015, which aims to promote the University’s doctoral students’ communication skills. Anaïs Espinoso is member of our project team, and she presented her work which forms an integral part of this project.

Chimera states in multiplex networks: Chameleon-like across-layer synchronization (2023)

Ralph G. Andrzejak , Anaïs Espinoso.

Different across-layer synchronization types of chimera states in multilayer networks have been discovered recently. We investigate possible relations between them, for example, if the onset of some synchronization type implies the onset of some other type. For this purpose, we use a two-layer network with multiplex inter-layer coupling. Each layer consists of a ring of non-locally coupled phase oscillators. While oscillators in each layer are identical, the layers are made non-identical by introducing mismatches in the oscillators’ mean frequencies and phase lag parameters of the intra-layer coupling. We use different metrics to quantify the degree of various across-layer synchronization types. These include phase-locking between individual interacting oscillators, amplitude and phase synchronization between the order parameters of each layer, generalized synchronization between the driver and response layer, and the alignment of the incoherent oscillator groups’ position on the two rings. For positive phase lag parameter mismatches, we get a cascaded onset of synchronization upon a gradual increase of the inter-layer coupling strength. For example, the two order parameters show phase synchronization before any of the interacting oscillator pairs does. For negative mismatches, most synchronization types have their onset in a narrow range of the coupling strength. Weaker couplings can destabilize chimera states in the response layer toward an almost fully coherent or fully incoherent motion. Finally, in the absence of a phase lag mismatch, sufficient coupling turns the response dynamics into a replica of the driver dynamics with the phases of all oscillators shifted by a constant lag.

Phase irregularity: A conceptually simple and efficient approach to characterize electroencephalographic recordings from epilepsy patients (2022)

Anaïs Espinoso, Ralph G. Andrzejak.

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. Allphase-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 non focal 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 non focal 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.