Methods and Resources

Building blocks for simulation of M/EEG activity and connectivity with MNE-Python

MEEGsim is a Python package that provides template waveforms for simulating M/EEG data with known ground truth source activity. In addition, it simplifies the manipulation of relevant simulation parameters (e.g., signal-to-noise ratio and source connectivity). As a result, the users can focus on what to simulate, not on how to implement the simulation. The package is compatible with MNE-Python and re-uses the forward and inverse modeling functionality provided by MNE.

GitHub repository: https://github.com/ctrltz/meegsim
Documentation: https://meegsim.readthedocs.io/en/stable/

Authors: Nikolai Kapralov, Alina Studenova, Dr. Mina Jamshidi Idaji.

Nonlinear interaction decomposition (NID): A method for separation of cross-frequency coupled sources in human brain

Github repository: https://github.com/minajamshidi/NID
Accompanying paper: https://doi.org/10.1016/j.neuroimage.2020.116599

Author: Dr. Mina Jamshidi Idaji.

Harmoni: A tool for removing harmonic for MEEG connectivity

HARMONI is an open-source Python package for eliminating/suppressing the spurious phase synchronization due to the non-sinusoidal shape of the neuronal oscillations. It can be used in connectivity pipelines for investigating within- and cross-frequency phase synchronization with electrophysiological data.

Github repository: https://github.com/harmonic-minimization/harmoni
Accompanying paper: https://doi.org/10.1016/j.neuroimage.2022.119053

Author: Dr. Mina Jamshidi Idaji.