A Student's Guide to Randomization Statistics for Multichannel Event-Related Potentials Using Ragu

• Published in: Frontiers in neuroscience Vol. 12; p. 355
• Main Authors: Habermann, Marie, Weusmann, Dorothea, Stein, Maria, Koenig, Thomas
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 19.06.2018; Frontiers Media S.A
• Subjects: Brain research; EEG; Electromagnetism; ERP; Event-related potentials; Grammatical subject; Hypotheses; Language; Medical imaging; microstates; N400; Neuroscience; Ragu; randomization statistics; Second language learning; Semantics; Statistical analysis; Topography; Switzerland; ERP; N400; Ragu; microstates; EEG; randomization statistics
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2018.00355

In this paper, we present a multivariate approach to analyze multi-channel event-related potential (ERP) data using randomization statistics. The MATLAB-based open source toolbox (Ragu) provides, among other methods, a test for topographic consistency, a topographic analysis of variance, t-mapping and microstate analyses. Up to two within-subject factors and one between-subject factor, each with an open number of levels, can be defined and analyzed in Ragu. Ragu analyses include all sensor signals and no a-priori models have to be applied during the analyses. Additionally, periods of significant effects can be controlled for multiple testing using global overall statistics over time. Here, we introduce the different alternatives to apply Ragu, based on a step by step analysis of an example study. This example study examined the neural activity in response to semantic unexpected sentence endings in exchange students at the beginning of their stay and after staying in a foreign-language country for 5 months.