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Using cluster-based permutation tests to estimate MEG/EEG onsets: How bad is it?

Guillaume A Rousselet1

  • 1School of Psychology and Neuroscience, College of Medical, Veterinary and Life, Sciences, University of Glasgow, Glasgow, UK.

The European Journal of Neuroscience
|December 1, 2024
PubMed
Summary
This summary is machine-generated.

Cluster-based statistical methods in electroencephalography (EEG) can overestimate effect onsets. New simulations show improved bias reduction strategies and hierarchical bootstrap confidence intervals offer more accurate localization of brain activity.

Keywords:
EEGMEGMonte Carlo simulationcluster inferencecorrection for multiple comparisonsfalse discovery ratefamilywise error rateonset estimationpermutation

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Biostatistics

Background:

  • Magneto- and electroencephalography (EEG) research aims to localize neural effects in space and time.
  • Cluster-based statistical methods are popular for correcting multiple comparisons in EEG data, but have limitations in precise temporal localization.

Purpose of the Study:

  • To compare the performance of cluster-sum inferences with other statistical methods for localizing effects in EEG data.
  • To evaluate strategies for reducing bias and variability in onset estimation.
  • To introduce a novel method for generating reliable confidence intervals for effect onsets.

Main Methods:

  • A simulation study comparing cluster-sum inferences with false discovery rate and familywise error rate controlling methods.
  • Implementation of bias reduction strategies: group calibration, group comparison, and binary segmentation.
  • Generation of onset hierarchical bootstrap confidence intervals.

Main Results:

  • The cluster-sum method replicates previously reported positive bias in onset estimation.
  • Despite bias, cluster-sum performs relatively well compared to pointwise p-value methods in terms of bias and variability.
  • Binary segmentation outperformed mass-univariate methods in simulations for onset detection.
  • Hierarchical bootstrap confidence intervals effectively integrate trial and participant variability.

Conclusions:

  • Standard cluster-sum methods in EEG analysis may lead to biased onset estimations.
  • Proposed strategies, particularly binary segmentation and hierarchical bootstrap confidence intervals, offer improved accuracy and reliability for localizing neural effects.
  • These advancements are crucial for precise interpretation of temporal dynamics in EEG research.