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 eyemovements. 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.

Overt and covert attention shifts to emotional faces: Combining EEG, eye tracking, and a go/no‐go paradigm

“…Filtering processes were done with a second‐order Butterworth bandpass filter with a high‐pass boundary of 0.01 Hz and a low‐pass boundary of 25 Hz. Very low high‐pass filters were used to avoid filter distortions in data with task‐relevant eye movements (Kulke & Kulke, 2020). To remove 50‐Hz line noise, the CleanLine plugin (Mullen, 2012) was used; note that this was applied after the low‐pass filter of 25 Hz was applied because some residual line noise may remain even after the low‐pass filter was applied.…”

Enhancing Precision in Human Neuroscience

“…Preprocessing Preprocessing steps such as filtering improve the precision of EEG data by removing highfrequency noise but may also have unpredictable effects on downstream analyses, affect the temporal resolution of the data, and introduce artifacts (Kulke & Kulke, 2020;Liesefeld, 2018;Rousselet, 2012;Tanner et al, 2015;Vanrullen, 2011;Widmann & Schröger, 2012). We recommend to rely on validated and standardized (semi-)automatic preprocessing pipelines that are appropriate for the nature of the data and the specific research question (see Kulke & Kulke, 2020;Liesefeld, 2018;Rousselet, 2012). If researchers decide to screen for artifacts manually instead, we recommend documenting manual scoring procedures and evaluating inter-rater consistency.…”

Enhancing precision in human neuroscience

“…Preprocessing steps such as filtering improve the precision of EEG data by removing high-frequency noise, but can also have unpredictable effects on downstream analyses, affect the temporal resolution of the data, and introduce artifacts ( Kulke and Kulke, 2020 ; Liesefeld, 2018 ; Rousselet, 2012 ; Tanner et al, 2015 ; Vanrullen, 2011 ; Widmann and Schröger, 2012 ). We recommend using validated and standardized (semi-)automatic preprocessing pipelines that are appropriate for the nature of the data and the specific research question (see Kulke and Kulke, 2020 ; Liesefeld, 2018 ; Rousselet, 2012 ). If researchers decide to screen for artifacts manually instead, we recommend documenting manual scoring procedures and evaluating inter-rater consistency.…”