neurora

#NeuroRA

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.

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 Neural Pattern Similarity (NPS), Spatiotemporal Pattern Similarity (STPS), Inter-Subject Correlation (ISC), Classification-based EEG Decoding and a novel cross-temporal RSA (CTRSA) on NeuroRA.

Installation

pip install neurora

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 Neural Pattern Similarity (NPS)

• Calculate the Spatiotemporal Neural Pattern Similarity (STPS)

• Calculate the Inter-Subject Correlation (ISC)

• Calculate the Representational Dissimilarity Matrix (RDM)

• Calculate the Cross-Temporal RDM (RDM)

• Calculate the Representational Similarity based on RDMs

• One-Step Realize Representational Similarity Analysis (RSA)

• Conduct Cross-Temporal RSA (CTRSA)

• Conduct Classification-based EEG decoding

• 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
Demo 2
Demo 3

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: [e-mail] / [e-mail]

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