Predictive models of the EEG activity based on eye movements during reading

<b>Objective</b>. This study aimed to explore the interrelationship between eye movements and EEG activity and determine which measure has better quality to predict reading efficiency. <b>Methods</b>. The study involved measuring eye movements and EEG from a limited number of leads while 40 healthy adults were reading a novel fragment. Machine learning models (CatBoost, KNN, linear regression) were then applied to predict EEG from eye movements, eye movements from EEG, and reading efficiency from both biological sources. <b>Results</b>. Throughout the study, CatBoost showed the best predictive quality. Of the EEG activity, the alpha-activity was the most accurately predicted from eye movements. Theta- and alpha-power were best predicted in posterior leads, while beta-power and engagement indices were best predicted in frontal leads. Then we predicted eye movements based on the EEG activity, the fixation number being the best-predicted feature. Finally, the EEG activity predicted reading efficiency much better than the eye movements data. <b>Discussion</b>. We describe our data in light of previous EEG experiments highlighting the role of posterior alpha and frontal beta bands in attention and reading processes. <b>Conclusions</b>. EEG activity and eye movements have been revealed to be interrelated, that is future studies can reduce the number of measurements in field settings, where limited technical equipment is available.