Metadata-Version: 1.2
Name: neurora
Version: 1.1.4.40
Summary: A Python Toolbox for Multimodal Neural Data Representation Analysis
Home-page: https://github.com/neurora/NeuroRA
Author: Zitong Lu
Author-email: zitonglu1996@gmail.com
Maintainer: Zitong Lu
Maintainer-email: zitonglu1996@gmail.com
License: MIT License
Description: ![ ](img/logo.jpg " ")
        
        #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** 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](https://zitonglu1996.github.io/NeuroRA/).
        
        You can read the [Documentation here](https://neurora.github.io/documentation/index.html) 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.
        - **[SciPy](https://www.scipy.org/scipylib/index.html)**: a package that provides many user-friendly and efficient numerical routines.
        - **[Scikit-learn](https://scikit-learn.org/stable/#)**: a Python module for machine learning.
        - **[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 Spatiotemporal Neural Pattern Similarity (STPS)
        
        - Calculate the Inter-Subject Correlation (ISC)
        
        - Calculate the Representational Dissimilarity Matrix (RDM)
        
        - Calculate the Representational Similarity based on RDMs
        
        - One-Step Realize Representational Similarity Analysis (RSA)
        
        - Statistical Analysis
        
        - Save the RSA result as a NIfTI file for fMRI
        
        - Classification-based EEG decoding
        
        - Visualization results of representational analysis
        
        ## 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](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ZitongLu1996/NeuroRA/blob/master/demo/NeuroRA_Demo1_colab.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/ZitongLu1996/NeuroRA/blob/master/demo/NeuroRA_Demo1.ipynb) |
        | Demo 2 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ZitongLu1996/NeuroRA/blob/master/demo/NeuroRA_Demo2_colab.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/ZitongLu1996/NeuroRA/blob/master/demo/NeuroRA_Demo2.ipynb) |
        | Demo 3 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ZitongLu1996/NeuroRA/blob/master/demo/NeuroRA_Demo3_colab.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/ZitongLu1996/NeuroRA/blob/master/demo/NeuroRA_Demo3.ipynb) |
        
        ## Road-Map of NeuroRA
        
        ![ ](img/road-map.png " ")
        
        ## 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@outlook.com
        
        >My personal homepage:
        >https://zitonglu1996.github.io
        
Platform: all
Classifier: Development Status :: 4 - Beta
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: Implementation
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Software Development :: Libraries
