iMap 4: An open source toolbox for the statistical fixation mapping of eye movement data with linear mixed modelling
A major challenge in modern eye movement research is to statistically map where observers are looking at. Compared to signals of contemporary neuroscience measures, such as M/EEG and fMRI, eye movement data are sparser with much larger variations across trials and participants. As a result, the implementation of a conventional linear modeling approach on two-dimensional fixation distributions often returns unstable estimations and underpowered results. Here, we tackled this issue by applying a pixel-wise Linear Mixed Models on the smoothed fixation data with each subject as a random effect. All the possible linear contrasts for the fixed effects (main effects, interactions, etc.) could be performed after the model fitting. Importantly, we introduced a novel spatial cluster test based on bootstrapping to assess the statistical significance of the linear contrasts. This approach is validated by using both experimental and Monte Carlo simulation data. We implemented this approach in an open source MATLAB toolbox - iMap 4 - with a userfriendly interface providing straightforward, easy to interpret statistical graphical outputs. iMap 4 matches the standards of the robust statistical neuroimaging methods. iMap 4 should thus provide an easy access to robust data-driven analyses for the statistical spatial mapping of eye movement data.