citations.bib

@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_cluster_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_robust_glm_2022,
	title = {Electroencephalography robust statistical linear modelling using a single weight per trial},
	volume = {2022},
	url = {https://apertureneuropub.cloud68.co/articles/51},
	doi = {10.52294/2e69f7cc-f061-40ad-ad77-017110464dfd},
	abstract = {Being able to remove or weigh down the influence of outlier data is desirable for any statistical model. While magnetic and electroencephalographic ({MEEG}) data are often averaged across trials per condition, it is becoming common practice to use information from all trials to build statistical linear models. Individual trials can, however, have considerable weight and thus bias inferential results (effect sizes as well as thresholded t/F/p maps). Here, rather than looking for univariate outliers, defined independently at each measurement point, we apply the principal component projection ({PCP}) method at each channel, deriving a single weight per trial at each channel independently. Using both synthetic data and open electroencephalographic ({EEG}) data, we show (1) that {PCP} is efficient at detecting a large variety of outlying trials; (2) how {PCP}-based weights can be implemented in the context of the general linear model ({GLM}) with accurate control of type 1 family-wise error rate; and (3) that our {PCP}-based weighted least squares ({WLS}) approach increases the statistical power of group analyses as well as a much slower iterative reweighted least squares ({IRLS}) approach, although the weighting scheme is markedly different. Together, our results show that {WLS} based on {PCP} weights derived from whole trial profiles is an efficient method to weigh down the influence of outlier {EEG} data in linear models.},
	pages = {51},
	number = {7},
	journaltitle = {Aperture Neuro},
	shortjournal = {Aperture Neuro},
	author = {Pernet, Cyril and Mas, Ignacio Suay and Rousselet, Guillaume and Martinez, Ramon and Wilcox, Rand and Delorme, Arnaud},
	urldate = {2022-05-13},
	date = {2022-03-22},
	langid = {english},
	file = {Pernet et al. - 2022 - Electroencephalography robust statistical linear m.pdf:D\:\\Personal_files\\Dropbox\\zotero\\storage\\4L43QYD7\\Pernet et al. - 2022 - Electroencephalography robust statistical linear m.pdf:application/pdf}
}

@article{pernet_pipeline_2021,
	title = {From {BIDS}-Formatted {EEG} Data to Sensor-Space Group Results: A Fully Reproducible Workflow With {EEGLAB} and {LIMO} {EEG}},
	volume = {14},
	issn = {1662-453X},
	url = {https://www.frontiersin.org/articles/10.3389/fnins.2020.610388/full},
	doi = {10.3389/fnins.2020.610388},
	shorttitle = {From {BIDS}-Formatted {EEG} Data to Sensor-Space Group Results},
	abstract = {Reproducibility is a cornerstone of scientific communication without which one cannot build upon each other’s work. Because modern human brain imaging relies on many integrated steps with a variety of possible algorithms, it has, however, become impossible to report every detail of a data processing workflow. In response to this analytical complexity, community recommendations are to share data analysis pipelines (scripts that implement workflows). Here we show that this can easily be done using {EEGLAB} and tools built around it. {BIDS} tools allow importing all the necessary information and create a study from electroencephalography ({EEG})-Brain Imaging Data Structure compliant data. From there preprocessing can be carried out in only a few steps using {EEGLAB} and statistical analyses performed using the {LIMO} {EEG} plug-in. Using Wakeman and Henson (2015) face dataset, we illustrate how to prepare data and build different statistical models, a standard factorial design (faces ∗ repetition), and a more modern trial-based regression approach for the stimulus repetition effect, all in a few reproducible command lines.},
	pages = {610388},
	journaltitle = {Frontiers in Neuroscience},
	shortjournal = {Front. Neurosci.},
	author = {Pernet, Cyril R. and Martinez-Cancino, Ramon and Truong, Dung and Makeig, Scott and Delorme, Arnaud},
	urldate = {2022-05-13},
	date = {2021-01-11},
	langid = {english},
	file = {Pernet et al. - 2021 - From BIDS-Formatted EEG Data to Sensor-Space Group.pdf:D\:\\Personal_files\\Dropbox\\zotero\\storage\\6D6EGLLZ\\Pernet et al. - 2021 - From BIDS-Formatted EEG Data to Sensor-Space Group.pdf:application/pdf}
}