Combining eye tracking with EEG: Effects of filter settings on EEG for trials containing task relevant eye-movements

Abstract

Co-registration of electroencephalography (EEG) and eye movements is becoming increasingly popular, as technology advances. This new method has several advantages, including the possibility of testing non-verbal populations and infants. However, eye movements can create artefacts in EEG data. Previous methods to remove eye-movement artefacts, have used high-pass filters before data processing. However, the role of filter settings for eye-artefact exclusion has not directly been investigated. The current study examined the effect of filter settings on EEG recorded in a dataset containing task-relevant eye movements. Part 1 models the effects of filters on eye-movement artifacts and part 2 demonstrates this effect on an EEG dataset containing task-relevant eye-movements. It shows that high-pass filters can lead to significant distortions and create artificial responses that are unrelated to the target. In conclusion, high-pass filter settings of 0.1 or lower can be recommended for EEG studies involving task-relevant eye movements. HighlightsO_LICo-registration of EEG and eye-tracking is gaining popularity C_LIO_LIHowever, eye movements can create artifacts in the EEG signal C_LIO_LIThe current paper models the effect of high pass filters on eye-movement artifacts C_LIO_LIHigh pass filters can induce large distortions in EEG data containing regular eye-movements C_LIO_LIThe distortion is affected by fixation duration and filter frequency C_LI