Related Experiment Video
Updated: Feb 5, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
Published on: November 1, 2019
Comparison of statistical tests in effective connectivity analysis of ECoG data
Ali Moharramipour1, Parham Mostame1, Gholam-Ali Hossein-Zadeh1
1School of ECE, College of Engineering, University of Tehran, Tehran, Iran.
Background:
The effects of statistical testing on the results of multivariate autoregressive (MVAR)-based effective connectivity analysis have not been adequately investigated, and it is still unclear which statistical test can provide the most accurate results.
New Methods:
Using simulated and real electrocorticographic (ECoG) data, we investigated the performance of three nonparametric statistical tests - Monte Carlo permutation, bootstrap resampling, and surrogate data method in MVAR-based effective connectivity analysis. Receiver operating characteristic (ROC) analysis and area under the ROC curve (AUC) were used to assess the performance of each statistical test method. In addition, we found optimal p-values for each method based on ROC analysis. Finally, we investigated the application of statistical tests on partial directed coherence analysis of ECoG data collected in a patient with epilepsy.
Results:
The bootstrap statistical test performed more accurately than other methods. The surrogate method slightly outperformed the Monte Carlo permutation method. Optimal p-values of statistical tests depended on signal-to-noise ratio (SNR) of data, and its value increased by reducing SNR of data. By considering the typical SNR range of electrophysiological data, we recommended an optimal p-value range for the application of each statistical test method.
Comparison With Existing Methods:
Limited studies have investigated the performance of statistical tests for MVAR-based effective connectivity analysis. For the first time, we have investigated the effects of baseline connections on the various performances of statistical tests.
Conclusions:
We recommend utilizing the bootstrap statistical test with p-value between 0.05 and 0.1 for effective connectivity analysis of ECoG data.
More Related Videos
Related Concept Videos
Statistical Software for Data Analysis and Clinical Trials
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
Statistical Hypothesis Testing
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
Multiple Comparison Tests
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
Bioequivalence Data: Statistical Interpretation
Statistical Methods for Analyzing Epidemiological Data

