Temporal segmentation of EEG based on functional connectivity network structure.

ABSTRACT: In the study of brain functional connectivity networks, it is assumed that a network is built from a data window in which activity is stationary. However, brain activity is non-stationary over sufficiently large time periods. Addressing the analysis electroencephalograph (EEG) data, we propose a data segmentation method based on functional connectivity network structure. The goal of segmentation is to ensure that within a window of analysis, there is similar network structure. We designed an intuitive and flexible graph distance measure to quantify the difference in network structure between two analysis windows. This measure is modular: a variety of node importance indices can be plugged into it. We use a reference window versus sliding window comparison approach to detect changes, as ind

SUBMITTER: Xu Z 

PROVIDER: S-EPMC10730570 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature