EEG preprocessing
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Four participants had to be excluded from the EEG analyses due to technical failure during the recordings resulting in a total of ten participant whereby the behavioral results in this subgroup did not differ from the entire sample of 14 participants.EEG data processing and statistical analysis were run in MATLAB 2020b (The MathWorks Inc., Massachusetts, USA) and MNE Python (Additional EEG artefacts were detected and discarded as follows: First, bad channels were identified visually and corrected with the spherical spline method, which projects the sensor locations onto a unit sphere and interpolates the signal at the bad sensor locations based on the signals at surrounding artefact-free locations (Data were cut into epochs ranging from 500 ms before fixation cue onset to 100 ms after stimulus onset to allow edge artifacts to subside outside the actual window of interest. Only epochs in which a subsequent correct antisaccade was performed were further analyzed. Trial rejections during eye-tracking and EEG preprocessing resulted in an average number of correctly executed antisaccade trials of 68 ± 49 in the DBS-off state, 79 ± 40 in the 130 Hz-DBS and 80 ± 32 for 60 Hz-DBS condition remaining for time–frequency analysis. For statistical testing, the number of trials was randomly equalized between all three conditions within each subject using the “equalize_epoch_counts” function implemented in MNE Python to maintain a constant signal-to-noise-ratio within subjects.