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This folder contains the data (EEG, ERPs and behavioral data prepared to be combined with the EEG data), the scripts (including the results of the EEG analyses) and helper functions to perform the EEG analyses. EEG data were prepared using custom-made MATLAB v8.5 (The Mathworks, Natic, MA, USA) scripts together with EEGLAB v13.4 (Delorme & Makeig, 2004) functions. They are saved as `.bdf` in the subfolder `Daten/EEG`. In the folder `scripts`, the Matlab file called `batch_sfs_osf.m` allows us to prepare the event-related potentials (ERPs). To perform the analyses included in this file, the following toolboxes/files are necessary: - EEGLAB - Measures of Effect Size - the helper functions saved in the folder `helper functions` In the file `batch_sfs_osf.m`, the following functions must be comment/uncomment to prepare the data as follows: - The continuous EEG signal was band-pass filtered excluding frequencies below 0.1 Hz and above 40 Hz (the function `eeg1_import` in `batch_sfs_osf.m`). Here, the behavioral data saved in the subfolder `Daten/Behavior` are linked to the EEG data - Epochs were extracted ranging from −200 ms before to 600 ms after stimulus onset with a baseline interval from –200 to 0 ms (the function `eeg2_epoch_stim` in `batch_sfs_osf.m`). - Electrodes were interpolated using spherical spline interpolation if it met either the joint probability criterion (threshold 5) or the kurtosis criterion (threshold 5) in EEGLAB's channel rejection routine (the function `eeg3a_electrodeInterpolation` in `batch_sfs_osf.m`). - Epochs were removed that contained activity exceeding +/−300 μV in any channel except AF1, Fp1, Fpz, Fp2, AF8 (to prevent exclusion of blink artifacts, which were corrected in a later stage) and whose joint probability deviated more than 5 standard deviations from the epoch mean (the function `eeg3b_trialRejection` in `batch_sfs_osf.m`). - To correct for eye blinks and muscular artefacts, an infomax-based ICA (Bell & Sejnowski, 1995) was computed and components with time courses and topographies typical of these artefacts were removed after visual inspection (the functions `eeg4_ICA` & `eeg5b_manualICrejection` in `batch_sfs_osf.m`). For each analysis, the transformed data were saved the subfolder `Daten/EEG`. <br>Then, we created ERPs, that is: - we averaged epochs separately for each trial type and each participant. Similar to RTs, we removed epochs including an error and following an error as well as epochs in which the RT was faster than 2.5 SD from the mean RT and slower than 2.5 SD from the mean RT for each trial type and participant (the function `erp1_average` in `batch_sfs_osf.m`). - Difference waves were also computed (the function `erp2_diffwaves` in `batch_sfs_osf.m`). Here, the transformed data were saved under: - `Daten/ERP/StimLocked/raw` if raw data were used as dependent measure ERPs were computed separately for analyses on no-catch trials (`no`) including all trials (`all`). Different figures were created: - mean stimulus-locked ERPs waveforms at the electrode(s) of interest using the function `visual_comperp` in `batch_sfs_osf.m` - scalp topography using the function `visual_topoplot` in `batch_sfs_osf.m` These figure are saved in the folder `scripts` in matlab format and their names include `fig`. <br> For the null-hypothesis significance testing (NHST) and Bayesian approach, R and SPSS file in `.txt` format were created using `testing_meanAmplitude` in the file `batch_sfs_osf.m`. These are saved in the folder `scripts` and their names include `dat`. Both NHST and Bayesian approaches were analyzed with R within RStudio with the script called `sfs_eeg_combi` in the folder `scripts`. Results are in `.txt` format in the file called `eeg_results_statbf_raw`. Descriptives results are also saved in the files `eeg_descriptives_raw`. Plots were computed with the script called `sfs_eeg_combi_plots`. These are saved in the folder `scripts` and their names start with `fig`.
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