ABSTRACT
Fixation-related potentials (FRPs), neural responses aligned to saccade offsets, are a promising tool to study the dynamics of attention and cognition under natural viewing conditions. In the past, four methodological problems have complicated the analysis of such combined eye-tracking/EEG experiments: (i) the synchronization of data streams, (ii) the removal of ocular artifacts, (iii) the condition-specific temporal overlap between the brain responses evoked by consecutive fixations, (iv) and the fact that numerous low-level stimulus and saccade properties also influence the post-saccadic neural responses. While effective solutions exist for the first two problems, the latter ones are only beginning to be addressed. In the current paper, we present and review a unified framework for FRP analysis that allows us to deconvolve overlapping potentials and control for linear and nonlinear confounds on the FRPs. An open software implementation is provided for all procedures. We then demonstrate the advantages of this approach for data from three commonly studied paradigms: face perception, scene viewing, and natural sentence reading. First, for a traditional ERP face recognition experiment, we show how deconvolution can separate stimulus-ERPs from overlapping muscle and brain potentials produced by small (micro)saccades on the face. Second, in scene viewing, we isolate multiple non-linear influences of saccade parameters on the FRP. Finally, for a natural sentence reading experiment using the boundary paradigm, we show how it is possible to study the neural correlates of parafoveal preview after removing spurious overlap effects caused by the associated difference in average fixation time. Our results suggest a principal way of measuring reliable fixation-related brain potentials during natural vision.
Footnotes
The authors would like to thank Linda Gerresheim and Anna Pajkert for their help with collecting some of the datasets used here as well as Anna Lisa Gert, Peter König, and Lisa Spiering for feedback on this work. Collection of the reading dataset was supported by a grant from Deutsche Forschungsgemeinschaft (DFG FG 868-A2).
Text, figures, and references were revised/updated. New stand-alone figure 3 explains massive univariate approach. Visualized effect of overlap correction in scene viewing data. Improved models and figures for the scene viewing (Fig. 7) and the reading (Fig. 8) examples. Corrected error in reporting number of spline predictors in some examples.
