The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Nonparametric statistical testing of EEG- and MEG-data
Open accessAbstract
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 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]
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“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
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“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]
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“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]
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“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
In the citing paper
“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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Citations
Cites
Nonparametric permutation tests for functional neuroimaging: A primer with examples
Nichols, T. E., & Holmes, A. P. (2002) · Human Brain Mapping · doi:10.1002/hbm.1058 · Cited by 6,256
Reading Senseless Sentences: Brain Potentials Reflect Semantic Incongruity
Marta Kutas, A., & Hillyard, S. A. (1980) · Science · doi:10.1126/science.7350657 · Cited by 5,029
Spectral Analysis for Physical Applications
Percival, D. B., & Walden, A. T. (1993) · Cambridge University Press eBooks · doi:10.1017/cbo9780511622762 · Cited by 2,148
Electrophysiology reveals semantic memory use in language comprehension.
Kutas, M., & Federmeier, K. D. (2000) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(00)01560-6 · Cited by 2,124
Detecting activations in PET and fMRI: levels of inference and power
Friston, K. J., Holmes, A., Poline, J.-B., Price, C. J., & Frith, C. D. (1996) · NeuroImage · doi:10.1006/nimg.1996.0074 · Cited by 1,148 · cited 1×
“This issue was introduced in the fMRI-literature by Holmes (1994) (see also, Friston et al., 1996).”
MethodBullmore, E. T., Suckling, J., Overmeyer, S., Rabe-Hesketh, S., Taylor, E., & Brammer, M. J. (1999) · IEEE Transactions on Medical Imaging · doi:10.1109/42.750253 · Cited by 1,088
Nonparametric Analysis of Statistic Images from Functional Mapping Experiments
Holmes, A. P., Blair, R. C., Watson, J. D. G., & Ford, I. (1996) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1097/00004647-199601000-00002 · Cited by 894
Permutation Methods: A Basis for Exact Inference
Ernst, M. (2004) · Statistical Science · doi:10.1214/088342304000000396 · Cited by 858
Significance Testing of Difference Potentials
Guthrie, D., & Buchwald, J. S. (1991) · Psychophysiology · doi:10.1111/j.1469-8986.1991.tb00417.x · Cited by 751
Statistical methods of estimation and inference for functional MR image analysis
Bullmore, E. T., Brammer, M. J., Williams, S., Rabe‐Hesketh, S., Janot, N., David, A. S., Mellers, J. D. C., Howard, R. J., & Sham, P. C. (1996) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910350219 · Cited by 683
An alternative method for significance testing of waveform difference potentials
Blair, R. C., & Karniski, W. M. (1993) · Psychophysiology · doi:10.1111/j.1469-8986.1993.tb02075.x · Cited by 604
Validating cluster size inference: random field and permutation methods
Hayasaka, S. (2003) · NeuroImage · doi:10.1016/j.neuroimage.2003.08.003 · Cited by 491
Halgren, E., Dhond, R. P., Christensen, N., Petten, C. K. V., Marinković, K., Lewine, J. D., & Dale, A. M. (2002) · NeuroImage · doi:10.1006/nimg.2002.1268 · Cited by 427
Distinct time courses of word and context comprehension in the left temporal cortex
Helenius, P. (1998) · Brain · doi:10.1093/brain/121.6.1133 · Cited by 281
Nonparametric statistical testing of coherence differences
Maris, E., Schoffelen, J., & Fries, P. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.02.011 · Cited by 278
Self-citationCombining voxel intensity and cluster extent with permutation test framework
Hayasaka, S., & Nichols, T. E. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.04.035 · Cited by 262
Singh, K. D., Barnes, G. R., & Hillebrand, A. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00249-0 · Cited by 222
Human gamma-band activity: A window to cognitive processing
Kaiser, J., & Lutzenberger, W. (2005) · Neuroreport · doi:10.1097/00001756-200502280-00001 · Cited by 218
A comparison of random field theory and permutation methods for the statistical analysis of MEG data
Pantazis, D., Nichols, T. E., Baillet, S., & Leahy, R. M. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2004.09.040 · Cited by 196
Dynamics of Gamma-Band Activity during an Audiospatial Working Memory Task in Humans
Lutzenberger, W., Ripper, B., Busse, L., Birbaumer, N., & Kaiser, J. (2002) · Journal of Neuroscience · doi:10.1523/jneurosci.22-13-05630.2002 · Cited by 190
Simos, P. G., Basile, L., & Papanicolaou, A. C. (1997) · Brain Research · doi:10.1016/s0006-8993(97)00349-1 · Cited by 155
Dynamics of gamma-band activity in human magnetoencephalogram during auditory pattern working memory
Kaiser, J., Ripper, B., Birbaumer, N., & Lutzenberger, W. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00350-1 · Cited by 148
Multivariate Permutation Tests
Babu, G. J. (2002) · Technometrics · doi:10.1198/004017002320256468 · Cited by 135
Randomization tests for ERP topographies and whole spatiotemporal data matrices
Maris, E. (2003) · Psychophysiology · doi:10.1111/j.1469-8986.2003.00139.x · Cited by 132
Self-citationCortical Activation during Spoken-Word Segmentation in Nonreading-Impaired and Dyslexic Adults
Helenius, P., Salmelin, R. H., Service, E., Connolly, J. F., Leinonen, S., & Lyytinen, H. (2002) · Journal of Neuroscience · doi:10.1523/jneurosci.22-07-02936.2002 · Cited by 130
Galán, L., Biscay, R., Rodríguez, J. L., Perez-Abalo, M. C., & Rodríguez, R. (1997) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/s0013-4694(96)95155-3 · Cited by 111
Improving permutation test power for group analysis of spatially filtered MEG data
Chau, W., McIntosh, A. R., Robinson, S. E., Schulz, M., & Pantev, C. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.007 · Cited by 96
Karniski, W. M., Blair, R. C., & Snider, A. D. (1994) · Brain Topography · doi:10.1007/bf01187710 · Cited by 88
Kaiser, J., Hertrich, I., Ackermann, H., & Lutzenberger, W. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.10.042 · Cited by 67
Kaiser, J., Lutzenberger, W., Preissl, H. T., Moßhammer, D., & Birbaumer, N. (2000) · Neuroscience Letters · doi:10.1016/s0304-3940(00)00921-6 · Cited by 32
Statistical tests for fMRI based on experimental randomization
Raz, J. A., Zheng, H., Ombao, H., & Turetsky, B. I. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00115-0 · Cited by 30
Statistical detection of between-group differences in event-related potentials
Achim, A. (2001) · Clinical Neurophysiology · doi:10.1016/s1388-2457(01)00519-3 · Cited by 18
Maris, E. (1998) · Psychometrika · doi:10.1007/bf02295437 · Cited by 3
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Research works printed but not linked: 1
- Holmes AP. Statistical issues in functional brain mapping. PhD thesis. University of Glasgow; 1994.
Cited by
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Oostenveld, R., Fries, P., Maris, E., & Schoffelen, J.-M. (2011) · Computational Intelligence and Neuroscience · doi:10.1155/2011/156869 · Cited by 10,323
Self-citationMEG and EEG data analysis with MNE-Python
Gramfort, A. (2013) · Frontiers in Neuroscience · doi:10.3389/fnins.2013.00267 · Cited by 3,882
ERPLAB: an open-source toolbox for the analysis of event-related potentials
López‐Calderón, J., & Luck, S. J. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00213 · Cited by 2,599
MNE software for processing MEG and EEG data
Gramfort, A., Luessi, M., Larson, E., Engemann, D. A., Strohmeier, D., Brodbeck, C. J., Parkkonen, L. T., & Hämäläinen, M. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.10.027 · Cited by 2,035
Vinck, M., Oostenveld, R., van Wingerden, M., Battaglia, F., & Pennartz, C. M. A. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.055 · Cited by 1,753
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Metadata
- 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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