Estimation of Task-Related Dynamic Brain Connectivity via Data Inflation and Classification Model Explainability.

ABSTRACT: Study of brain function often involves analyzing task-related switching between intrinsic brain networks, which connect various brain regions. Functional brain connectivity analysis methods aim to estimate these networks but are limited by the statistical constraints of windowing functions, which reduce temporal resolution and hinder explainability of highly dynamic processes. In this work, we propose a novel approach to functional connectivity analysis through the explainability of EEG classification. Unlike conventional methods that condense raw data into extracted features, our approach inflates raw EEG data by decomposition into meaningful components that explain processes in the application domain. To uncover the brain connectivity that affects classification decisions, we introduce a

SUBMITTER: Rogelj P 

PROVIDER: S-EPMC12133929 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature