Skip to main content

A Python Toolbox for Multimodal Neural Data Representation Analysis

Project description

#NeuroRA2

A Python Toolbox of Representational Analysis from Multimodal Neural Data

Overview

Representational Similarity Analysis (RSA) has become a popular and effective method to measure the representation of multivariable neural activity in different modes.

NeuroRA2(an updated version of NeuroRA) is an easy-to-use toolbox based on Python, which can do some works about RSA among nearly all kinds of neural data, including behavioral, EEG, MEG, fNIRS, sEEG, ECoG, fMRI and some other neuroelectrophysiological data. In addition, users can do Inter-Subject Correlation (ISC), Classification-based EEG Decoding and a novel cross-temporal RSA (CTRSA) on NeuroRA.

Installation

pip install neurora2

Paper

Lu, Z., & Ku, Y. (2020). NeuroRA: A Python toolbox of representational analysis from multi-modal neural data. Frontiers in Neuroinformatics. 14:563669. doi: 10.3389/fninf.2020.563669

Website & How to use

See more details at the NeuroRA website.

You can read the Documentation here or download the Tutorial here to know how to use NeuroRA.

Required Dependencies:

  • Numpy: a fundamental package for scientific computing.
  • SciPy: a package that provides many user-friendly and efficient numerical routines.
  • Scikit-learn: a Python module for machine learning.
  • Matplotlib: a Python 2D plotting library.
  • NiBabel: a package prividing read +/- write access to some common medical and neuroimaging file formats.
  • Nilearn: a Python module for fast and easy statistical learning on NeuroImaging data.
  • MNE-Python: a Python software for exploring, visualizing, and analyzing human neurophysiological data.

Features

  • Calculate the Representational Dissimilarity Matrix (RDM)

  • Calculate the Cross-Temporal RDM (RDM)

  • Calculate the Representational Similarity based on RDMs

  • Conduct Cross-Temporal RSA (CTRSA)

  • Conduct Classification-based EEG decoding

  • Calculate the Inter-Subject Correlation (ISC)

  • Conduct Statistical Analysis

  • Save the RSA result as a NIfTI file for fMRI

  • Plot the results

Demos

There are several demos for NeuroRA, and you can see them in /demos/.. path (both .py files and .ipynb files are provided).

Run the Demo View the Demo
Demo 1 Open In Colab View the notebook
Demo 2 Open In Colab View the notebook
Demo 3 Open In Colab View the notebook

About NeuroRA

Noteworthily, this toolbox is currently only a test version. If you have any question, find some bugs or have some useful suggestions while using, you can email me and I will be happy and thankful to know.

My email address: zitonglu1996@gmail.com / zitonglu@mit.com

My personal homepage: https://zitonglu1996.github.io

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

neurora2-2.0.0.2.tar.gz (5.0 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

neurora2-2.0.0.2-py3-none-any.whl (5.0 MB view details)

Uploaded Python 3

File details

Details for the file neurora2-2.0.0.2.tar.gz.

File metadata

  • Download URL: neurora2-2.0.0.2.tar.gz
  • Upload date:
  • Size: 5.0 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.18

File hashes

Hashes for neurora2-2.0.0.2.tar.gz
Algorithm Hash digest
SHA256 9d0a90c769939f0e4be01fae9d384405b656d5d8a09dfe62bf48bb57b7c61076
MD5 75375a35b7041930508752c4b93760d3
BLAKE2b-256 4655348fa00b19e846ed29c00e43ad4f2fd096c66505aa5fec2ddac559d233f6

See more details on using hashes here.

File details

Details for the file neurora2-2.0.0.2-py3-none-any.whl.

File metadata

  • Download URL: neurora2-2.0.0.2-py3-none-any.whl
  • Upload date:
  • Size: 5.0 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.18

File hashes

Hashes for neurora2-2.0.0.2-py3-none-any.whl
Algorithm Hash digest
SHA256 8190bfed7274bb57097799b51475e5315935f27e36d3ff589a51036496450405
MD5 953ae4f72c940edffb0cc355a0b2546d
BLAKE2b-256 0b5f2560f46c01dc461a9f1a94d0ac7eafbe56b88bfe3668d4e38b4ffb62d52f

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page