Preprocessed EEG Data for for the Study "Brain Network Differences in Second Language Learning Depend on Individual Competencies"

This dataset contains preprocessed EEG data used in the manuscript "Brain Network Differences in Second Language Learning Depend on Individual Competencies" (currently under review). Data contents (Zenodo): - Preprocessed EEG data (`.set`, `.fdt`) for all participants, organized by session: - `pre/` — EEG data before learning - `post/` — EEG data after learning The filename structure is `[SubjectID]_pre.set/.fdt` and `[SubjectID]_post.set/.fdt`. Subject IDs match those in: - `demographic.xlsx` - `vocabulary_test_data.csv` - `Fit Segmentation.04.(06).GroupXColumnsAsFactors.csv` (located on OSF) - GFP/GMD input for RAGU: - `ragu_exp.mat` — MATLAB data structure used for topographic ANOVA --- Preprocessing Details: Preprocessing was performed using EEGLAB (version 2024.1; Delorme & Makeig, 2004) and ERPLAB (version 12.00; Lopez-Calderon & Luck, 2014). The following steps were applied: - Downsampling to 250 Hz - Band-pass filtering (0.1–35 Hz) - Continuous artifact rejection using a peak-to-peak threshold of 150 µV (400 ms window) - Interpolation of bad channels - Re-referencing to the common average - Epoching from -200 ms to 800 ms (stimulus-locked) - Baseline correction (-200 to 0 ms) - Epoch-level artifact rejection using a fixed ±150 µV amplitude threshold - Detection of eye-blink artifacts using frontal electrodes (Fp1, Fp2) via cross-covariance detection (`pop_artblink`) On average, 302 ± 68 pre-learning and 305 ± 64 post-learning epochs were retained per participant after preprocessing. The data were recorded with a 64-channel RNet system (Brain Products GmbH). Full details on EEG acquisition (electrode layout, referencing, impedance control, recording hardware) are provided in the manuscript. --- Related project: The full analysis pipeline, including:- behavioral and EEG analysis scripts,- figures and statistics,- `demographic.xlsx`,- `vocabulary_test_data.csv`, and- `Fit Segmentation.04.(06).GroupXColumnsAsFactors.csv` … is available via the OSF Repository --- Contact: For questions or additional access, contact: [e-mail]