There are 4 repositories under mne-python topic.
Automated rejection and repair of bad trials/sensors in M/EEG
Automatically process entire electrophysiological datasets using MNE-Python.
Connectivity algorithms that leverage the MNE-Python API.
Analyze and manipulate EEG data using PyEEGLab.
Representational Similarity Analysis on MEG and EEG data
EEG inverse solution with artificial neural networks. This package works with MNE-Python data structures for easy integration into your MNE-based M/EEG code
Neuropycon package of functions for electrophysiology analysis, can be used from graphpype and nipype
Preprocessing Pipelines for EEG (MNE-python), fMRI (nipype), MEG (MNE-python/autoreject) data
Analysis scripts for group analysis of MEG data in both Python and MATLAB associated with Andersen, L.M., 2018. Group Analysis in MNE-Python of Evoked Responses from a Tactile Stimulation Paradigm: A Pipeline for Reproducibility at Every Step of Processing, Going from Individual Sensor Space Representations to an across-Group Source Space Representation. Front. Neurosci. 12. https://doi.org/10.3389/fnins.2018.00006 and Andersen, L. M. Group Analysis in FieldTrip of Time-Frequency Responses: A Pipeline for Reproducibility at Every Step of Processing, Going From Individual Sensor Space Representations to an Across-Group Source Space Representation. Front. Neurosci. 12, (2018). https://10.3389/fnins.2018.00261
Quick guide and tutorial to scientific data python programming
MNE-preprocessing is a python repository to reduce artifacts based on basic and unanimous approaches step by step from electroencephalographic (EEG) raw data.
EEG Motor Imagery Classification Using CNN, Transformer, and MLP
A beginner's guide to analyze MEG data using MNE-Python
group sequential tests for neuroimaging
A simple open source Python package for I/O between Cartool and Python
tutorial and practice of EEG (Electroencephalogram) analysis, filtering, data I/O via MNE-Python (Minimum Norm Estimation)
The main objective of this project is to recognise positive, negative and neutral emotions (CNN). Creating artificial signals using generative adversarial networks (GANs)
A U-Net for approximating the MEG inverse problem
The python codes for the Analyzing Neural Time Series Data, Mike X Cohen (2012) MIT Press
NeuroIDBench: An Open-Source Benchmark Framework for the Standardization of Methodology in Brainwave-based Authentication Research
Wrapper for MNE that makes fNIRS data analysis easier
Sparsity enables subcortical source estimation, Krishnaswamy et al, PNAS 2017
Introduction to EEG analysis course using MNE-Python
Predicting Cognitive Workload in Flight Simulations using EEG Spectral and Connectivity Features: Repository for Research Code and Results
laura: Local Auto-Regressive Average: A linear solution to the M/EEG inverse problem
Extracting power bands (theta, delta, alpha, beta bands) from the EEG signal using MNE and YASA Python