MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python

Machine learning for NeuroImaging in Python

Workflows and interfaces for neuroimaging packages

DIPY is the paragon 3D/4D+ medical imaging library in Python. Contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging.

Deep Learning Toolkit for Medical Image Analysis

Train AI models efficiently on medical images using any framework

Python package to access a cacophony of neuro-imaging file formats

Slicer extensions index

Deep learning software to decode EEG, ECG or MEG signals

Brain Imaging Data Structure (BIDS) Specification

A fast medical imaging analysis library in Python with algorithms for registration, segmentation, and more.

Brain Imaging Analysis Kit

Automated Quality Control and visual reports for Quality Assessment of structural (T1w, T2w) and functional MRI of the brain

TE-dependent analysis of multi-echo fMRI

MNE-BIDS is a Python package that allows you to read and write BIDS-compatible datasets with the help of MNE-Python.

Software platform for clinical neuroimaging studies

Reorganising NIfTI files from dcm2niix into the Brain Imaging Data Structure

Website for the Brain Imaging Data Structure standard.

Learn about the Neumorphic engineering process of creating large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures.

A toolbox for comparing brain maps