A test of hypotheses for random graph distributions built from EEG data

TL;DR

This paper introduces a non-parametric statistical test to compare random graph distributions, specifically applied to EEG-derived brain network data, enhancing methods for neural interaction analysis.

Contribution

It develops an efficient non-parametric hypothesis test for comparing random graph samples, with application to EEG brain network data.

Findings

Test effectively distinguishes different graph distributions.

Applied successfully to EEG brain network data.

Demonstrates potential for neural interaction studies.

Abstract

The theory of random graphs is being applied in recent years to model neural interactions in the brain. While the probabilistic properties of random graphs has been extensively studied in the literature, the development of statistical inference methods for this class of objects has received less attention. In this work we propose a non-parametric test of hypotheses to test if two samples of random graphs were originated from the same probability distribution. We show how to compute efficiently the test statistic and we study its performance on simulated data. We apply the test to compare graphs of brain functional network interactions built from electroencephalographic (EEG) data collected during the visualization of point light displays depicting human locomotion.