neurora 1.0.7.31

A Python Toolbox for Multimode Neural Data Representation Analysis


A Python Toolbox of Representational Analysis from Multimode 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, ECoG, electrophysiological and fMRI data.

## Installation > pip install neurora

## Website & How to use See more details at the [NeuroRA website](https://zitonglu1996.github.io/NeuroRA/).

You can read or download the [Tutorial here](https://zitonglu1996.github.io/NeuroRA/neurora/Tutorial.pdf) to know how to use NeuroRA.

## Required Dependencies:

• [Numpy](http://www.numpy.org): a fundamental package for scientific computing

• [Matplotlib](https://matplotlib.org): a Python 2D plotting library

• [NiBabel](https://nipy.org/nibabel/): a package prividing read +/- write access to some common medical and neuroimaging file formats

• [Nilearn](https://nilearn.github.io/): a Python module for fast and easy statistical learning on NeuroImaging data

• [MNE-Python](https://mne.tools/): a Python software for exploring, visualizing, and analyzing human neurophysiological data

## Features

• Calculate the Neural Pattern Similarity (NPS)

• Calculate the Representational Dissimilarity Matrix (RDM)

• Calculate the Representational Similarity based on RDMs

• One-Step Realize Representational Similarity Analysis (RSA)

• Save the RSA result as a NIfTI file for fMRI

• Visualization for RSA results

## Paper

Lu, Z., & Ku, Y. NeuroRA: A Python toolbox of representational analysis from multi-modal neural data. (bioRxiv: https://doi.org/10.1101/2020.03.25.008086)

## 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