Artifact Rejection
We implemented a fully automated artifact rejection framework (for details, see (Murty et al. , 2020, 2021; Murty & Ray, 2022), in which outliers were detected as repeats with deviation from the mean signal in either time or frequency domains by more than 6 standard deviations, and subsequently electrodes with too many (30%) outliers were discarded. Subsequently, stimulus repeats that were deemed bad in more than 10% of the other electrodes (see below) or in the visual electrodes were discarded. This gave rise to a set of good electrodes and common bad repeats for each subject. Overall, this led us to reject 15.33±0.36% (mean±SEM) stimulus repeats. We also rejected electrodes with high impedance (>25kΩ). Finally, as in our previous studies, we computed slopes for the baseline power spectrum between 56-84Hz range for each unipolar electrode and rejected electrodes whose slopes were less than 0. Based on these criteria, 15.30±0.72% (mean±SEM; min: 0, median: 12.5%, max: 60.9%) electrodes were labelled bad. Some subjects who had predominantly bad electrodes (>40 electrodes) were rejected later in the pipeline.
Eye position was monitored using EyeLink 1000 (SR Research Ltd), sampled at 500Hz. Any eye-blinks or change in eye position outside a 5º fixation window during -0.5s to 0.75s from stimulus onset were noted as fixation breaks, which were removed offline. This led to a rejection of 16.54±0.059% (mean±SEM) stimulus repeats. Overall, we rejected 31.88±0.81% (mean±SEM) of the repeats (min: 8.4%, median: 30.1%, max: 73.2%).