Longitudinal changes in aperiodic and periodic activity in electrophysiological recordings in the first seven months of life
This repository provides analysis code to analyze longitudinal changes in aperiodic activity in infant EEG data. The repository code recreates results and figures from the following manuscript:
Schaworonkow N & Voytek B: Longitudinal changes in aperiodic and periodic activity in electrophysiological recordings in the first seven months of life. Developmental Cognitive Science (2021). doi:10.1016/j.dcn.2020.100895
The results are based on following available openly available data set: infant EEG dataset and the corresponding data sheet.
From the associated articles:
- Xiao R, Shida-Tokeshi J ,Vanderbilt DL, Smith BA: Electroencephalography power and coherence changes with age and motor skill development across the first half year of life. PLOSOne (2018).
- Hooyman A, Kayekjian D, Xiao R, Jiang C, Vanderbilt DL, Smith BA: Relationships between variance in electroencephalography relative power and developmental status in infants with typical development and at risk for developmental disability: An observational study. GatesOpenResearch (2018).
To reproduce the results, the data set should be downloaded and placed in the folder data.
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: fooof and for computation of waveform features: bycycle. Specifically used versions can be seen in the requirements.txt. R-scripts use lme4 and ciTools.
To reproduce the figures from the command line, navigate into the code folder and execute make all. This will run through the preprocessing steps, the analysis of aperiodic exponents and the oscillatory burst analysis. The scripts can also be executed separately in the order described in the Makefile.