In Matlab (Mathworks, MA, USA), we set EEG sample values to zero in an interval disrupted by the TMS pulses (−2 to 65 msec in relation to TMS onset). Next, we interpolated (using a spline interpolation) the EEG samples set to zero (using data 250 msec before and after the interval set to zero), without affecting EEG samples outside this 67-msec interval (the interpolated segment was of the same order as the rest

of the data), so we were able to further filter the data (Sadeh et al. 2011). After initial low-pass filtering (100 Hz) Inhibitors,research,lifescience,medical during recording, additional filters were applied after removal of the TMS artifact and data interpolation. High-pass filtering (0.5 Hz), additional low-pass filtering (30 Hz), and a notch filter (50 Hz) were used (doing the filtering before artifact removal would propagate the substantially Vandetanib VEGFR inhibitor stronger TMS artifact through

the data). To limit the spreading of the interpolated data, we used an infinite impulse response (IIR) Inhibitors,research,lifescience,medical filter kernel of limited length. Next, we down-sampled to 256 Hz, and subsequently re-referenced to central medical electrode (Cz). Non-TMS-related artifacts as eye movements were corrected on the basis of independent component analysis (Vigário 1997) and ocular correction (Gratton et al. 1983). Artifact Inhibitors,research,lifescience,medical correction was applied on all separate channels by removing segments outside the range of ±75 μV or with a voltage step exceeding 50 μV per sampling point. To increase spatial specificity and to filter out deep sources, we converted the data to spline Laplacian signals (Perrin et al. 1989). After conversion to spline Laplacian signals, trials were manually inspected and removed if irregularities due to interpolation were found. EEG data were baseline corrected Inhibitors,research,lifescience,medical by subtracting the average sample value across the 100 msec prior to stimulus presentation. Finally, all trials were averaged per condition. All preprocessing steps were done using Brain Vision Analyzer (BrainProducts, Gilching, Germany), ASA (ANT – ASA-Lab), and Matlab (Mathworks). We created an a priori pooling Inhibitors,research,lifescience,medical of electrodes to increase the signal-to-noise ratio and decrease the amount

of comparisons. We based our pooling (O1, O2, Oz, POz, PO3, PO4, PO5, PO6, PO7, and PO8) on previous literature showing neural correlates of figure–ground segregation in these channels (Scholte et al. 2008; Pitts et al. 2011) and where we expected the disruption of TMS would have an effect (Thut et al. 2003). Although we removed the TMS artifact from Drug_discovery our EEG data (see above), the TMS-evoked potential was still present in our data. To cancel out effects in our EEG data related to local dot displacement and the TMS-evoked potential, we subtracted ERPs on trials containing a homogenous stimulus from ERPs on trials containing a figure stimulus (stacks and frames collapsed, see Fig. 5) for each TMS condition separately (Thut et al. 2005; this Fahrenfort et al. 2007; Taylor et al. 2007; Sadeh et al. 2011).