A comparative study of the robustness of frequency--domain connectivity measures to finite data length

TL;DR

This study compares how different frequency-domain connectivity measures from EEG data are affected by data length and noise, revealing their reliability and false positive tendencies through simulations.

Contribution

It provides a systematic comparison of IC, gPDC, and fGC measures under various data lengths and noise conditions using numerical simulations.

Findings

IC is less sensitive to connectivity with small data samples

gPDC and fGC tend to produce more false positives

Biological noise impacts IC differently than gPDC and fGC

Abstract

In this work we use numerical simulation to investigate how the temporal length of the data affects the reliability of the estimates of brain connectivity from EEG time--series. We assume that the neural sources follow a stable MultiVariate AutoRegressive model, and consider three connectivity metrics: Imaginary part of Coherency (IC), generalized Partial Directed Coherence (gPDC) and frequency--domain Granger Causality (fGC). In order to assess the statistical significance of the estimated values, we use the surrogate data test by generating phase--randomized and autoregressive surrogate data. We first consider the ideal case where we know the source time courses exactly. Here we show how, expectedly, even exact knowledge of the source time courses is not sufficient to provide reliable estimates of the connectivity when the number of samples gets small; however, while gPDC and fGC tend to provide a larger number of false positives, the IC becomes less sensitive to the presence of connectivity. Then we proceed with more realistic simulations, where the source time courses are estimated using eLORETA, and the EEG signal is affected by biological noise of increasing intensity. Using the ideal case as a reference, we show that the impact of biological noise on IC estimates is qualitatively different from the impact on gPDC and fGC.