Nonparametric statistical testing of EEG- and MEG-data
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In this paper, we show how ElectroEncephaloGraphic (EEG) and MagnetoEncephaloGraphic (MEG) data can be analyzed statistically using nonparametric techniques. Nonparametric statistical tests offer complete freedom to the user with respect to the test statistic by means of which the experimental conditions are compared. This freedom provides a straightforward way to solve the multiple comparisons problem (MCP) and it allows to incorporate biophysically motivated constraints in the test statistic, which may drastically increase the sensitivity of the statistical test. The paper is written for two audiences: (1) empirical neuroscientists looking for the most appropriate data analysis method, and (2) methodologists interested in the theoretical concepts behind nonparametric statistical tests. For the empirical neuroscientist, a large part of the paper is written in a tutorial-like fashion, enabling neuroscientists to construct their own statistical test, maximizing the sensitivity to the expected effect. And for the methodologist, it is explained why the nonparametric test is formally correct. This means that we formulate a null hypothesis (identical probability distribution in the different experimental conditions) and show that the nonparametric test controls the false alarm rate under this null hypothesis.
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Correction for multiple comparisons at the electrode level was performed using cluster-based statistics implemented in Fieldtrip.
“Correction for multiple comparisons at the electrode level was performed using the cluster-based statistics implemented in Fieldtrip [50]”
From Increased Gamma Brainwave Amplitude Compared to Control in Three Different Meditation Traditions (2017) · cites this paper as [50]
A nonparametric cluster permutation test was applied to compare differences between groups and conditions while controlling for multiple comparisons.
“A nonparametric cluster permutation test was applied to compare differences between groups (monk rest vs. control rest) and conditions (monk meditation vs. monk rest) while controlling for multiple comparison (Maris and Oostenveld 2007).”
From Brain–Heart Interactions Underlying Traditional Tibetan Buddhist Meditation (2019) · cites this paper as Maris and Oostenveld 2007
Statistical significance of decoding performance was assessed via permutation tests with 1,000 permutations, corrected for multiple comparisons using cluster-based correction.
“Statistical significance of decoding performance was assessed via permutation test, randomly permuting the sample labels and repeating the decoding analysis 1,000 times, corrected for multiple comparisons using a cluster-based correction (cluster mass inference with cluster forming threshold at P < 0.05)105,106.”
From Adversarial testing of global neuronal workspace and integrated information theories of consciousness (2025) · cites this paper as [106]
Group-level one-sample t-tests per time point on MEG decoding accuracy were corrected for multiple comparisons using cluster-based correction.
“A group-level one-sample t-test per time point was performed on the decoding accuracy results, corrected for multiple comparisons using a cluster-based correction106.”
From Adversarial testing of global neuronal workspace and integrated information theories of consciousness (2025) · cites this paper as [106]
Cluster-based permutation analyses were used for global TEP data.
“Identified clusters were then compared to a permutation distribution generated using the Monte-Carlo method (10,000 permutations) and deemed significant if their cluster-statistic (sum of all t-values in the cluster) exceeded P < 0.05 (Maris and Oostenveld 2007).”
From Mild traumatic brain injury alters function in the dorsolateral prefrontal cortex: a TMS-EEG study (2025) · cites this paper as Maris and Oostenveld 2007
To avoid a priori spatiotemporal bias and account for multiple comparisons, non-parametric data driven spatiotemporal clustering was performed.
“To avoid a priori spatiotemporal bias and account for multiple comparisons, non-parametric data driven spatiotemporal clustering was performed.72, 73, 74”
From Altered spatiotemporal brain dynamics of interoception in behavioural-variant frontotemporal dementia (2025) · cites this paper as [74]
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- Authors
- Maris, E., & Oostenveld, R.
- Affiliations
- Radboud University Nijmegen (NL)
- Corresponding author
- Maris, Eric
- Venue
- Journal of Neuroscience Methods
- Publisher
- Elsevier BV
- Published
- 2007-08
- Volume
- 164
- Issue
- 1
- Pages
- 177-190
- ISSN
- 0165-0270 (ISSN-L)
- OpenAlex
- W2135894974
- Cited by
- 8,496
- References
- 33
- Citations by year
- 2026: 722 · 2025: 929 · 2024: 835 · 2023: 762 · 2022: 777 · 2021: 796 · 2020: 667 · 2019: 645 · 2018: 543 · 2017: 437 · 2016: 354 · 2015: 283 · 2014: 239 · 2013: 174 · 2012: 119
- FWCI
- 18.82
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Journal of Neuroscience Methods (publishedVersion)
- PubMed (publishedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- nonparametric tests, EEG signal analysis, MEG data analysis, statistical sensitivity, false alarm rate control
- MeSH terms
- Brain, Brain Mapping, Data Interpretation, Statistical, Electroencephalography, Evoked Potentials, Humans, Signal Processing, Computer-Assisted, Magnetoencephalography, Statistics, Nonparametric
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