mne-python

Automated rejection and repair of bad trials/sensors in M/EEG

Automatically process entire electrophysiological datasets using MNE-Python.

Realtime data analysis with MNE-Python

Representational Similarity Analysis on MEG and EEG data

Analyze and manipulate EEG data using PyEEGLab.

[DEPRECATED: use MNE-Python] Python module to stream and analyze EEG data in real-time

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

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

Quick guide and tutorial to scientific data python programming

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

A beginner's guide to analyze MEG data using MNE-Python

Installers for MNE-Python.

MNE-preprocessing is a python repository to reduce artifacts based on basic and unanimous approaches step by step from electroencephalographic (EEG) raw data.

A simple open source Python package for I/O between Cartool and Python

MNE-Python extension for VS Code

Reading Spike2 files for electromiography and preprocessing them

Repository for mne docker images

Introduction to processing of EEG data using Python on a remote cluster

A U-Net for approximating the MEG inverse problem

BrainVision EEG data classification using the MNE, Keras and the scikit-learn libraries.

Research in the area of Brain-Computer interfaces.

Sparsity enables subcortical source estimation, Krishnaswamy et al, PNAS 2017

A runner for the MNE BIDS Pipeline.

tutorial and practice of EEG (Electroencephalogram) analysis, filtering, data I/O via MNE-Python (Minimum Norm Estimation)

Electrophysiological MEG study of basic auditory function in infants