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# 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 Inter-Subject Correlation (ISC), Classification-based EEG Decoding and a novel cross-temporal RSA (CTRSA) on 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

Installation

pip install neurora

Documentation

You can read the Documentation 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

About NeuroRA

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