Ragu: A Free Tool for the Analysis of EEG and MEG Event-Related Scalp Field Data Using Global Randomization Statistics
By Thomas Koenig, Mara Kottlow, M. Stein, Lester Melie-García. Computational Intelligence and Neuroscience, 2011
320 citations recorded by the source corpus.
We present a program (Ragu; Randomization Graphical User interface) for statistical analyses of multichannel event-related EEG and MEG experiments. Based on measures of scalp field differences including all sensors, and using powerful, assumption-free randomization statistics, the program yields robust, physiologically meaningful conclusions based on the entire, untransformed, and unbiased set of measurements. Ragu accommodates up to two within-subject factors and one between-subject factor with multiple levels each. Significance is computed as function of time and can be controlled for type II errors with overall analyses. Results are displayed in an intuitive visual interface that allows further exploration of the findings. A sample analysis of an ERP experiment illustrates the different possibilities offered by Ragu. The aim of Ragu is to maximize statistical power while minimizing the need for a-priori choices of models and parameters (like inverse models or sensors of interest) that interact with and bias statistics.
Subfields: Cognitive Neuroscience
View Paper: https://www.semanticscholar.org/paper/032174b1206091ef7f072d97087ebe10087e0d91
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Citations in the corpus
- Towards the utilization of EEG as a brain imaging tool (2012, methodology): “This measure can be used to assess statistically significant topographic differences between conditions or groups through non-parametric randomization tests using uni-or multi-factorial designs (Murray et al., 2008; Koenig et al., 2011).”
- The role of auditory cortices in the retrieval of single‐trial auditory–visual object memories (2015, unknown): “First, the randomisation-based analysis of ERP topography (Koenig et al., 2011) identified significant differences across the three conditions over the 32–50, 236–270 and 336–368 ms post-stimulus periods.”
- The functional significance of EEG microstates - Associations with modalities of thinking (2016, unknown): “Multidimensional scaling (Koenig et al., 2011) of the maps for all four conditions revealed that object- and spatial-visualization deviated similarly from verbalization on the left–right and anterior–posterior map topography for microstate classes C and D, respectively.”