Academic Journal
Comparing methods for mass univariate analyses of human EEG: Empirical data and simulations.
| Τίτλος: | Comparing methods for mass univariate analyses of human EEG: Empirical data and simulations. |
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| Συγγραφείς: | Tebbe AL; University of Florida, Department of Psychology, Laboratory for Brain, Body, and Behavior, Gainesville, FL, United States. Electronic address: atebbe@ufl.edu., Panitz C; University of Bremen, Germany., Keil A; University of Florida, Department of Psychology, Laboratory for Brain, Body, and Behavior, Gainesville, FL, United States. |
| Πηγή: | Journal of neuroscience methods [J Neurosci Methods] 2026 Feb; Vol. 426, pp. 110630. Date of Electronic Publication: 2025 Nov 20. |
| Τύπος έκδοσης: | Journal Article; Comparative Study |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier/North-Holland Biomedical Press Country of Publication: Netherlands NLM ID: 7905558 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-678X (Electronic) Linking ISSN: 01650270 NLM ISO Abbreviation: J Neurosci Methods Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Amsterdam, Elsevier/North-Holland Biomedical Press. |
| Ιατρικοί όροι (MeSH): | Electroencephalography*/methods , Brain*/physiology , Computer Simulation* , Signal Processing, Computer-Assisted*, Evoked Potentials/physiology ; Humans ; Bayes Theorem |
| Περίληψη: | Background: Electroencephalography (EEG) is a widely used method for investigating human brain dynamics. However, EEG analyses are frequently conducted with limited a priori knowledge regarding locations or latencies of meaningful statistical effects. This makes it difficult for researchers to form regions of interest (ROIs), which are then analyzed using traditional statistical models such as analysis of variance. To address this, mass univariate analyses have become a valuable complement to ROI-based approaches. These methods attempt to correct for multiple comparisons while mitigating the risk of false positives and false negatives, thus enabling statistical inference in high-dimensional EEG data. New Method: Here, we review and evaluate different approaches for delineating spatial and temporal effect boundaries in three datasets. Specifically, we focus on permutation-based approaches and their Bayesian alternatives to address within-subjects condition differences in i) steady-state evoked responses, ii) event-related potentials, and iii) time-frequency data. Results: Overall, simulation results showed variability in the number of datapoints indicating statistical condition differences. Comparison With Existing Methods: Cluster-based permutation tests provide a relatively liberal approach to correct for multiple comparisons across domains, with high sensitivity for detecting large effects. In contrast, the permutation-based t Conclusions: These findings provide insights into the strengths and limitations of current mass univariate approaches with the goal of supporting more informed decision-making in the analysis of EEG data. (Copyright © 2025 Elsevier B.V. All rights reserved.) |
| Competing Interests: | Declaration of Competing Interest The authors declare no conflicts of interest. |
| Σχόλια: | Update of: bioRxiv. 2025 Nov 20:2025.11.19.689266. doi: 10.1101/2025.11.19.689266.. (PMID: 41332598) |
| Contributed Indexing: | Keywords: Bayesian statistics; ERP; Effect size; Multiple comparison; Permutation tests; Power; Time frequency |
| Entry Date(s): | Date Created: 20251122 Date Completed: 20251211 Latest Revision: 20251222 |
| Update Code: | 20260130 |
| DOI: | 10.1016/j.jneumeth.2025.110630 |
| PMID: | 41274354 |
| Βάση Δεδομένων: | MEDLINE |
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