The data are baseline-corrected epochs for time intervals from -500 ms (baseline) to plus 2000 ms around the TMS pulses in each condition. Each participant's data is saved in a separate file, e.g. ‘[participant_numer]_EEG_data.mat’. The file is a Matlab structure array containing eight fields: six matrices containing neural time series data for each condition, a time stamp vector and information pertaining to the EEG channels. Data was extracted from EEGLab's EEG structure array (Delorme & Makeig, 2014) in the following way: data.cond1 = EEG_bin{1}.data; data.cond2 = EEG_bin{2}.data; data.cond3 = EEG_bin{3}.data; data.cond4 = EEG_bin{4}.data; data.cond5 = EEG_bin{5}.data; data.cond6 = EEG_bin{6}.data; data.time_stamp = EEG.times; data.channel_info = EEG.chanlocs; data.cond: The number behind data.cond corresponds to the condition number introduced above. The neural time series data is stored in a separate matrix for each condition. The matrix has the dimensions channel x time x trial, e.g. [66, 5160, 120]. 66: There are 66 channels. 5160: Each epoch is 2.5 seconds long. At a sampling rate of 2048 Hz, this gives us a total of 5160 time points. 120: We originally recorded 120 trials for each condition for each participant, however, due to artefact rejection, this number is smaller in the dataset, for instance for participant 17, condition 1, there are 110 trials. data.time_stamp: Each participant's datafile also includes a vector called time_stamp. It contains the sampling moments related to the timing of the TMS pulse. For instance, time 1 in the data matrix corresponds to -509.77 ms prior to TMS. The TMS pulse occurs at time point 1045, and data is set to the linear function mentioned earlier between time points 993 (-26 ms) and 1066 (+11 ms). data.channel_info: This contains some info about the individual channels 1 to 66, such as label and location coordinates, taken directly from the standard EEGLab structure corresponding to the cap setup we used.