Research Directions for Methodological Improvement of the Statistical Analysis of Electroencephalography Data Collected in NeuroIS

Marc Fredette (), Élise Labonté-LeMoyne (), Pierre-Majorique Léger (), François Courtemanche () and Sylvain Sénécal ()
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Marc Fredette: Tech3Lab
Élise Labonté-LeMoyne: Tech3Lab
Pierre-Majorique Léger: Tech3Lab
François Courtemanche: Tech3Lab
Sylvain Sénécal: Tech3Lab

A chapter in Information Systems and Neuroscience, 2015, pp 201-206 from Springer

Abstract: Abstract This proposed research will study and improve the statistical methodology used with neurophysiological data collected from subjects using information systems (IS). This research thus aims to provide guidelines and propose new statistical models constructed explicitly for the analysis of electroencephalography (EEG) data in IS research, where the number of EEG trials is often limited to preserve the ecological validity of the experiment. Two new modeling strategies are proposed: first, we will model explicitly the correlation between repeated trials by finding appropriate correlation structures. Secondly, we will reduce the measurement’s error by using explicitly the cyclic behavior of an electrical brain signal. These new models will then be taken into account to derive new formulas for sample size determination.

Keywords: Toeplitz structure; Exponential structure; Periodic functions; Hierarchical likelihood; Statistical analysis (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-319-18702-0_27

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DOI: 10.1007/978-3-319-18702-0_27

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