@article{Pernet_limo_2011,
	title = {{LIMO} {EEG}: A Toolbox for Hierarchical {LInear} {MOdeling} of {ElectroEncephaloGraphic} Data},
	volume = {2011},
	issn = {1687-5265, 1687-5273},
	url = {http://www.hindawi.com/journals/cin/2011/831409/},
	doi = {10.1155/2011/831409},
	shorttitle = {{LIMO} {EEG}},
	pages = {1--11},
	journaltitle = {Computational Intelligence and Neuroscience},
	author = {Pernet, Cyril R. and Chauveau, Nicolas and Gaspar, Carl and Rousselet, Guillaume A.},
	urldate = {2014-04-01},
	date = {2011},
	langid = {english},
	file = {Pernet et al. - 2011 - LIMO EEG A Toolbox for Hierarchical LInear MOdeli.pdf:D\:\\Personal_files\\Dropbox\\zotero\\storage\\FP89MSCR\\Pernet et al. - 2011 - LIMO EEG A Toolbox for Hierarchical LInear MOdeli.pdf:application/pdf}
}

@article{Pernet_robust_corr_2013,
	title = {Robust Correlation Analyses: False Positive and Power Validation Using a New Open Source Matlab Toolbox},
	volume = {3},
	issn = {1664-1078},
	url = {http://www.frontiersin.org/Quantitative_Psychology_and_Measurement/10.3389/fpsyg.2012.00606/abstract},
	doi = {10.3389/fpsyg.2012.00606},
	shorttitle = {Robust Correlation Analyses},
	journaltitle = {Frontiers in Psychology},
	author = {Pernet, Cyril R. and Wilcox, Rand and Rousselet, Guillaume A.},
	urldate = {2013-12-18},
	date = {2013},
	file = {fpsyg-03-00606.pdf:D\:\\Personal_files\\Dropbox\\zotero\\storage\\CMVFEN2H\\fpsyg-03-00606.pdf:application/pdf}
}

@article{Pernet_cluster_stats_2015,
	title = {Cluster-based computational methods for mass univariate analyses of event-related brain potentials/fields: A simulation study},
	volume = {250},
	issn = {01650270},
	url = {http://linkinghub.elsevier.com/retrieve/pii/S0165027014002878},
	doi = {10.1016/j.jneumeth.2014.08.003},
	shorttitle = {Cluster-based computational methods for mass univariate analyses of event-related brain potentials/fields},
	pages = {85--93},
	journaltitle = {Journal of Neuroscience Methods},
	author = {Pernet, C.R. and Latinus, M. and Nichols, T.E. and Rousselet, G.A.},
	urldate = {2016-02-23},
	date = {2015-07},
	langid = {english},
	file = {Pernet et al. - 2015 - Cluster-based computational methods for mass univa.pdf:D\:\\Personal_files\\Dropbox\\zotero\\storage\\SNDISGKP\\Pernet et al. - 2015 - Cluster-based computational methods for mass univa.pdf:application/pdf}
}

@article{Pernet_NHST_2017,
	title = {Null hypothesis significance testing: a guide to commonly misunderstood concepts and recommendations for good practice},
	volume = {4},
	doi = {10.12688/f1000research.6963.5},
	pages = {621},
	journaltitle = {F1000Research},
	author = {Pernet, C.R.},
	date = {2017},
	file = {Pernet - 2017 - Null hypothesis significance testing a guide to c.pdf:D\:\\Personal_files\\Dropbox\\zotero\\storage\\V9Y3FA7C\\Pernet - 2017 - Null hypothesis significance testing a guide to c.pdf:application/pdf}
}

@article{Maris_cluster_2007,
title = "Nonparametric statistical testing of EEG- and MEG-data",
journal = "Journal of Neuroscience Methods",
volume = "164",
number = "1",
pages = "177 - 190",
year = "2007",
issn = "0165-0270",
doi = "https://doi.org/10.1016/j.jneumeth.2007.03.024",
url = "http://www.sciencedirect.com/science/article/pii/S0165027007001707",
author = "Eric Maris and Robert Oostenveld",
keywords = "Nonparametric statistical testing, Hypothesis testing, EEG, MEG, Multiple comparisons problem",
abstract = "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."
}


@article{Bellec_psom_2012,
	title = {The pipeline system for Octave and Matlab ({PSOM}): a lightweight scripting framework and execution engine for scientific workflows},
	volume = {6},
	issn = {1662-5196},
	url = {http://www.frontiersin.org/neuroinformatics/10.3389/fninf.2012.00007/abstract},
	doi = {10.3389/fninf.2012.00007},
	abstract = {The analysis of neuroimaging databases typically involves a large number of inter-connected steps called a pipeline. The pipeline system for Octave and Matlab ({PSOM}) is a flexible framework for the implementation of pipelines in the form of Octave or Matlab scripts. {PSOM} does not introduce new language constructs to specify the steps and structure of the workflow. All steps of analysis are instead described by a regular Matlab data structure, documenting their associated command and options, as well as their input, output, and cleaned-up files. The {PSOM} execution engine provides a number of automated services: (1) it executes jobs in parallel on a local computing facility as long as the dependencies between jobs allow for it and sufficient resources are available; (2) it generates a comprehensive record of the pipeline stages and the history of execution, which is detailed enough to fully reproduce the analysis; (3) if an analysis is started multiple times, it executes only the parts of the pipeline that need to be reprocessed. {PSOM} is distributed under an open-source {MIT} license and can be used without restriction for academic or commercial projects. The package has no external dependencies besides Matlab or Octave, is straightforward to install and supports of variety of operating systems (Linux, Windows, Mac). We ran several benchmark experiments on a public database including 200 subjects, using a pipeline for the preprocessing of functional magnetic resonance images ({fMRI}). The benchmark results showed that {PSOM} is a powerful solution for the analysis of large databases using local or distributed computing resources.},
	number = {7},
	journaltitle = {Frontiers in Neuroinformatics},
	author = {Bellec, Pierre and Lavoie-Courchesne, Sébastien and Dickinson, Phil and Lerch, Jason and Zijdenbos, Alex and Evans, Alan C},
	date = {2012}
}