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Details on the different functions and usage can be found on the wiki pages here while this is a step by step tutorial.
Important notice
Whatever you display using LIMO plotting functions, all the variables are returned in the Matlab workspace. For instance, if you plot all channels vs time (ERP results), then the raw statistical map and the significance mask are returned. If you plot a time course, that time course with confidence interval is returned, etc. You may type "who" on the Matlab command line to see these variables.
The tutorial is using Wakeman and Henson (2015) face data. In short, famous, unfamiliar and scrambled faces were presented, and repeated immediately or later. Subjects had to do a judgment task orthogonal to the design to keep them engaged. The EEG channels were extracted and preprocessed. Data can be found here in BIDS format.
This tutorial assumes you are using the latest EEGLAB version (2020.0 or later) that uses STUDY to link with LIMO tools.
The tutorial is split into 4 main sections (see also tab on the right for direct access to subsections)
- Preprocessing
- Within Subject Categorical Designs
- Between subject designs (coming soon)
- Design that include continuous regressors at the 1st and/or 2nd level (coming soon)
- getting the data
- preprocessing
- Within Subject Categorical Designs intro
- One way repeated measures ANOVA (Famous, Unfamiliar, Scrambled faces as conditions)
- One way repeated measures ANOVA revised (Famous, Unfamiliar, Scrambled faces as 1st level contrasts)
- Summary statistics to measure and report effects and effect sizes
- One sample t-test (contrasting Full Faces vs Scrambled Faces at the subject level)
- Summary statistics of differences
- Two-ways ANOVA (Faces x Repetition)
- Paired t-test (Famous vs Unfamiliar)