#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
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Calculate the Representational Dissimilarity Matrix (RDM)
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Calculate the Cross-Temporal RDM (RDM)
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Calculate the Representational Similarity based on RDMs
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Conduct Cross-Temporal RSA (CTRSA)
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Conduct Classification-based EEG decoding
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Calculate the Inter-Subject Correlation (ISC)
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Conduct Statistical Analysis
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Save the RSA result as a NIfTI file for fMRI
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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 |  |
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| Demo 2 | |
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| Demo 3 | |
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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
1 maintainer
Release files for neurora 2.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| neurora-2.0.1.tar.gz | 5.0 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| neurora-2.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 10.1 MB
Release files / neurora-2.0.1.tar.gz
| Download URL | neurora-2.0.1.tar.gz |
|---|---|
| Size | 5.0 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/6.2.0 CPython/3.10.18
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Release files / neurora-2.0.1-py3-none-any.whl
| Download URL | neurora-2.0.1-py3-none-any.whl |
|---|---|
| Size | 5.0 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.10.18
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