Automatically process entire electrophysiological datasets using MNE-Python.

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

Connectivity algorithms that leverage the MNE-Python API.

Analyze and manipulate EEG data using PyEEGLab.

A research repository of deep learning on electroencephalographic (EEG) for Motor imagery(MI), including eeg data processing(visualization & analysis), papers(research and summary), deep learning models(reproduction and experiments).

Representational Similarity Analysis on MEG and EEG data

Realtime data analysis with MNE-Python

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

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

Neuropycon package of functions for electrophysiology analysis, can be used from graphpype and nipype

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 Repres…

Preprocessing Pipelines for EEG (MNE-python), fMRI (nipype), MEG (MNE-python/autoreject) data

Quick guide and tutorial to scientific data python programming

EEG Motor Imagery Classification Using CNN, Transformer, and MLP

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

Estimate/compute high-frequency oscillations (HFOs) from iEEG data that are BIDS and MNE compatible using a scikit-learn-style API.

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

Repository for mne docker images

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