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Scripts and data files for the Proactive Selective Suppression task Create a folder called 'scripts' and download all the MATLAB scripts (.m files) in there. The behavioral data is in behavior.zip. Unzip the file behavior.zip inside the scripts folder. Create a folder called 'data' inside the scripts folder and unzip all the data files (data_2_5.zip, data_6_9.zip, data_10_13.zip, data_14_17.zip) in the same. The scripts use two sets for EEG data files for the analysis, which is inside the data_x_x.zip file. One is named ‘sbjXX_fts_filt_pre_chan.set’, which is the file after pre-processing but before the noisy channel rejection and the manual stretch rejection steps. This is used for identifying events and markers during the entire task. The second dataset is the ‘sbjXX_fts_filt_rej.set’ which is the dataset after channel and manual stretch rejection. There are three scripts which are needed to produce the results seen in the paper. All the scripts were run in MATLAB R2016b. The EEGLAB version used was 14_1_1b which is also provided as a eeglab14_1_1b.zip file (must also be unzipped inside the scripts folder). Fieldtrip toolbox is also needed for generating the group level ERSP maps, provided as fieldtrip-master.zip and also needs to be inside the scripts folder. 1. GED_power_behavior_analysis.m: This scripts runs the frequency-specific generalized eigenvalue decomposition (GED), to get the sensorimotor spatial filters for both Maybe stop left vs Maybe stop right (a right spatial filter) and Maybe Stop right vs Maybe Stop left (a left spatial filter). The lines to change for doing GED for MSL vs MSR (for getting right sensorimotor components) or MSR vs MSL (for getting left sensorimotor components) are 129 and 130. We can visualize the filter forward model for each of the spatial filter. The script also computes the power time series for both the MSL and MSR trials. It also computes the correlation between the mean beta power and the probe alt RTs and go cue RTs. The MATLAB cell labelled ‘Power time series and Trial level changes in beta power vs Relationship to Probe RTs and GO cue RTs’ generates the power time series for each subject and the relationship to the go and probe RTs. Generates plots seen in Figure 4B. The MATLAB cell labelled ‘Power time course for all subjects combined’ generates the plot seen in Figure 3C and 3D. 2. GED_xValid_leave_in_out.m: This scripts performs the leave in out cross validation on the selected filters. The data is split into 90% train and 10% test and then the GED is performed on the 90% of the data and tested on the remaining 10%. The validated spatial filter is selected based on the spatial correlation to the original filter selected. It plots the validated power time course for all subjects. It generates the plot seen in Figure 3E and 3F. 3. GED_ersps.m: This script runs the event-related spectral perturbations for the maybe stop and the go trials for either the left or the right spatial filters. It uses EEGLAB function newtimef.m to generate the individual subject ERSPs. It also computes the group significance FDR corrected maps using the fieldtrip toolbox. It generates the plots seen in Figure 5.