A Multilayer Network-Based Method for Brain Connectivity Analysis from EEG Data

Multilayer networks (MLNs) have emerged as a critical tool in the field of medicine, particularly in neuroscience, owing to their capacity to model the complex interactions between brain regions across multiple dimensions. Unlike traditional single-layer network approaches, which typically focus on functional or structural connectivity within a single frequency band, MLN provides a richer, more comprehensive framework that captures the dynamic and multi-frequency nature of brain activity. In this work, we propose the development of a pipeline for the design and analysis of multilayer brain networks based on electroencephalogram (EEG) data. The primary object is to explore how MLNs can be utilized to analyze brain activity by capturing both intra- and inter-frequency interactions, that coordinate the different neural processes. The EEG data used in this study come from a cohort of 75 patients, including 25 healthy subjects, 25 with psychogenic non-epileptic seizures (PNES), and 25 with epilepsy. The results revealed significant differences in both the structure of the graphical network representation and the multilayer network analysis across the groups studied. These findings underscore the potential of multilayer networks (MLNs) to offer valuable insights into the distinct network patterns associated with various neurological conditions, providing a promising framework for advancing research into the complexity of brain network interactions.