zenodoopen

Strategy-based motor learning decreases the post-movement β power

This publication contains the following data, in connection with the manuscript entitled "Strategy-based motor learning decreases the post-movement β power", by Korka et al. (2023): 1- raw EEG and kinematics data files (for these, please see Version 1); 2 - task and stimulation files; 3 - analysis files. Task and stimulation files: - 2 experimental file formats, each corresponding to one condition: .exp - each .exp file calls on different scenarios (.sce) files: <ul> <li>“train1” and “train2” correspond to task instructions regarding the experimental set-up and the general task (please see instructions in the Supplementary Material associated with the paper).</li> <li>“COMPENSATE_DEMO” and “IGNORE_DEMO” correspond to demonstrating the rotation in each condition; the rotation is constantly switched on for the duration of these training blocks.</li> <li>“COMPENSATE_TRAIN” and “IGNORE_TRAIN” correspond to training for the actual experiment including a probabilistic rotation (i.e., same as in the experimental blocks, but fewer trials).</li> <li>Finally, “COMPENSATE” and “IGNORE” scenarios represent the actual experimental blocks for each condition.</li> </ul> Analysis files -main analysis files: <ul> <li>ecEEG_kinematics – processes the relevant kinematics parameters by reading in the raw kinematics datafiles.</li> <li>ecEEG_eeg_preproc – reads in and preprocesses EEG raw data files; main steps steps include: filtering, segmenting the data into epochs (and matching each epoch with the kinematics data storing movement parameters), rejection of epochs containing artifacts based on visual inspection, rejection of channels containing extreme amplitudes, Independent Component Analysis (ICA) for detecting eye- and muscle-related artifacts, interpolation of channels, automated trial rejection after ICA.</li> <li>ecEEG_eeg_freq and ecEEG_eeg_freq_baseline – computes time-frequency analyses for the PMBR data and baseline (inter-trial-interval) data, respectively + re-references data to a common average.</li> <li>ecEEG_eeg_freq_2 – excludes marked trials based on task performance, kinematics parameters, and EEG rejection criteria + organizes the data from all participants into a unique structure (for PMBR and ITI data, in turn).</li> <li>ecEEG_change_plots – produces the EEG figures displayed in the manuscript + exports data for statistics.</li> </ul> - auxiliary analysis files: <ul> <li>ecEEG_ica – if called on, computes the ICA</li> <li>VMB_neighbours – defines neighbors for each channel, which are necessary for interpolation</li> <li>ecEEG_trialreject – automated trial rejection based on kurtosis</li> <li>topoplotbasic_ml2012_varchans – called on for creating the topographic plots when visualizing the ICA components</li> <li>ecEEG_eeg_trial_rej_15thresh – used to determine the number of trials in which participants did not follow the task instructions (see manuscript for details)</li> </ul>

ShareScore

36/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship: 4
Harmonization: 4
Access: 16
Reuse readiness: 8
Engagement: 4