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

## TL;DR

This paper introduces a new method for analyzing brain connectivity using EEG data that improves both accuracy and interpretability of dynamic brain processes.

## Contribution

The novel dynamic influence data inflation (DIDI) method enhances interpretability and classification accuracy in EEG-based brain connectivity analysis.

## Key findings

- DIDI improves classification accuracy compared to conventional methods.
- Saliency maps reveal dynamic connectivity patterns influencing classification decisions.
- The approach is validated on two public datasets for motor movement and emotion classification.

## 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 new method of dynamic influence data inflation (DIDI), which extracts signals representing interactions between electrode regions. These inflated data are then classified using an end-to-end neural network classifier architecture designed for raw EEG signals. Saliency map estimation from trained classifiers reveals the connectivity dynamics affecting classification decisions, which can be visualized as dynamic connectivity support maps for improved interpretability. The methodology is demonstrated on two publicly available datasets: one for imagined motor movement classification and the other for emotion classification. The results highlight the dual benefits of our approach: in addition to providing interpretable insights into connectivity dynamics it increases classification accuracy.

The online version contains supplementary material available at 10.1007/s12021-025-09733-6.