Project repository for the BandRatios project, exploring frequency band ratio measures in electrophysiological data.
Band ratios are a common measure in neuroscientific investigations, in which the the ratio of average power between two frequency bands are examined as a feature of interest and potential biomarker in M/EEG, ECoG and LFP data analyses. These ratio measures are commonly applied in investigations within cognitive and clinical neuroscience. In this project, we explore the properties of band ratio measures, and how they relate to other spectral features.
You can follow along with this project by stepping through the whole thing, as outlined in the notebooks.
If you want to re-run the whole project, keep in mind that some parts are done by stand-alone scripts, available in the scripts folder. These scripts are also described in the notebooks.
This project is described in the preprint:
Donoghue T, Dominguez J & Voytek B. Electrophysiological Band-Ratio Measures Conflate
Changes in Periodic and Aperiodic Activity. bioRxiv. DOI: 10.1101/2020.01.11.900977
Direct Link: https://doi.org/10.1101/2020.01.11.900977
This project was written in Python 3 and requires Python >= 3.7 to run.
If you want to re-run this project, you will need some external dependencies.
Dependencies include 3rd party scientific Python packages:
You can get and manage these dependencies using the Anaconda distribution, which we recommend.
In addition, this project requires the following dependencies:
You can install the extra required dependencies by running:
pip install mne, fooof, lisc
This project repository is set up in the following way:
bratios/is a custom module containing the code for analyses and visualizations of this projectdata/is a collection of the project data, include simulation data, EEG data, and outputsfigures/holds all figures produced fromnotebooks/andscripts/notebooks/is a collection of Jupyter notebooks that step through the project and create the figuresscripts/contains stand alone scripts that run parts of the project
This project uses simulated data, literature data, and electroencephalography (EEG) data from an open-access repository.
The simulations are all done using the FOOOF tool and associated simulation framework. All simulated data reported upon in this project is available in the data/ folder.
The literature data was collected with LISC, a tool for collecting and analyzing literature data. All collected literature data used in this project is available in the data/ folder, and code to re-run the literature data collection is available in the notebooks/.
The EEG data is taken from the 'Multimodal Resource for Studying Information Processing in the Developing Brain' or MIPDB dataset. This dataset was collected and released by the ChildMind Institute. Raw data can be downloaded through their data portal. The processed power spectra, upon which we operate, and the calculated output measures for this project are collected and available in the data/ folder.