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Comparing methods for mass univariate analyses of human EEG: Empirical data and simulations
Anna-Lena Tebbe1, Christian Panitz2, Andreas Keil1
1University of Florida, Department of Psychology, Laboratory for Brain, Body, and Behavior, Gainesville, FL, United States.
Journal of Neuroscience Methods
|November 22, 2025
Summary
Mass univariate analyses improve electroencephalography (EEG) data interpretation by correcting for multiple comparisons. This review evaluates permutation and Bayesian methods for delineating effects in EEG, aiding researchers in statistical inference.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Statistical Analysis
Background:
- Electroencephalography (EEG) is vital for studying human brain dynamics.
- Traditional EEG analysis struggles with identifying specific effect locations and timings.
- Mass univariate analyses offer a solution for high-dimensional EEG data by correcting for multiple comparisons.
Purpose of the Study:
- To review and evaluate spatial and temporal effect boundary delineation methods in EEG.
- To compare permutation-based and Bayesian approaches for analyzing condition differences.
- To provide insights for informed decision-making in EEG data analysis.
Main Methods:
- Focus on permutation-based and Bayesian approaches for statistical inference.
- Evaluation across three datasets including steady-state evoked responses, event-related potentials, and time-frequency data.
- Comparison of cluster-based permutation tests and the tmax procedure.
Main Results:
- Simulation results indicated variability in identifying statistical condition differences.
- Cluster-based permutation tests are sensitive to large effects but liberal.
- The tmax procedure is the most conservative, while Bayesian methods depend on threshold selection.
Conclusions:
- Mass univariate approaches have varying strengths and limitations.
- Findings aid researchers in selecting appropriate statistical methods for EEG analysis.
- Informed decision-making is crucial for accurate interpretation of EEG data.
