Overcoming harmonic hurdles: genuine beta-band rhythms vs. contributions of alpha-band waveform shape
This repository provides analysis code to visualize beta-activity in a large open EEG dataset.
Schaworonkow, N.: Overcoming harmonic hurdles: genuine beta-band rhythms vs. contributions of alpha-band waveform shape. Imaging Neuroscience (2023). Retrieved from direct.mit.edu/imag/article/doi/10.1162/imag_a_00018
The results are based on following available openly available data set: "Leipzig Cohort for Mind-Body-Emotion Interactions" (LEMON dataset), from which we used the preprocessed EEG data. The associated data set research article:
- Babayan A et al.: A mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults. Scientific Data (2018).
The provided python3 scripts are using scipy and numpy for general computation, pandas for saving intermediate results to csv-files. matplotlib for visualization. For EEG-related analysis, the mne package is used. For computation of aperiodic exponents: specparam.
To reproduce the figures from the command line, navigate into the code folder and execute make all. This will run through the preprocessing steps and generate the figures. The scripts can also be executed separately in the order described in the Makefile. If data is not converted into fif-format yet, the proc0_convert_data_to_mne.py-script should be executed.