pyctrsa 0.1.0

A Python Toolbox for Cross-Temporal Representational Similarity Analysis-based Decoding on E/MEG Data

# PyCTRSA

[](https://pypi.org/project/pyctrsa/) [](https://opensource.org/licenses/MIT)

#### A Python toolbox for Cross-Temporal Representation Similarity Analysis (RSA)-based Decoding on EEG/MEG data

## Download > pip install pyctrsa

## Required Dependencies

• [Numpy](https://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.

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

• [NeuroRA](https://zitonglu1996.github.io/NeuroRA/): a Python toolbox for multimode neural data Representation Analysis.

## Hightlight In traditional RSA, we can only use a coding model RDM to fit the RDMs from neural data time by time. So, can we do cross-temporal decoding based on RSA?

CTRSA-based decoding is a new algorithm for cross-temporal E/MEG decoding by RSA. We use the neural data from two different time-points to establish a Cross-Temporal Representatonal Dissimilarity Matrix (RDM) corrsponding to time i and time j. By this train of thought, we can obtain Number_of_Times by Number_of_Times Cross-Temporal RDMs. Then we can establish a Coding Model RDM by the experimental hypothesis. Finally, we can calculate the similarity between this Coding Model RDM and the Number_of_Times by Number_of_Times Cross-Temporal RDMs and obtain the cross-temporal decoding results.

## Notes In PyCTRSA, you can not only calculate the cross-temporal similarities based on this novel methods to realize decoding, but also calculate the cross-temporal similarities based on neural data under two different conditions to see the similar data patterns between two conditions and calculate the cross-temporal similarities based on normal RDMs to see the similar representational patterns between different time-points.

## Features

1. Calculate the Cross-Temporal RDM (Novel here!)

> calculate CTRDMs for a single channel/subject > calculate CTRDMs for multi-channels&subejcts

2. Calculate the similarity between two CTRDMs (Novel here!)

> by Pearson Correlation/Spearman Correlation/Kendall tau Correlation/Cosine Similarity/Euclidean Distance

3. Calculate the Cross-Temporal Similarities

> calculate CTSimilarities between neural data under two conditions > calculate CTSimilarities based on normal RDMs > calculate CTSimilarities between CTRDMs and a Coding Model RDM (Novel here!)

4. Plot the Results

> plot the CTRDM > plot the CTSimilarities > plot the time-by-time similarities

## How to use PyCTRSA

| Run the Tutorial&Demo | View the Tutorial&Demo |

- | — | —- |

Learn here! | [](https://colab.research.google.com/github/ZitongLu1996/PyCTRSA/blob/master/docs/Tutorial_for_PyCTRSA_colab.ipynb) | [](https://nbviewer.jupyter.org/github/ZitongLu1996/PyCTRSA/blob/master/docs/Tutorial_for_PyCTRSA.ipynb) |

## Efficiency of using PyCTRSA

Here, we use a tutorial to compare the traditional classification-based decoding and novel cross-temporal RSA-based decoding below:

| Run the Tutorial | View the Tutorial |

- | — | —- |

Learn here! | [](https://colab.research.google.com/github/ZitongLu1996/PyCTRSA/blob/master/docs/Decoding_Classification_VS_CTRSA.ipynb) | [](https://nbviewer.jupyter.org/github/ZitongLu1996/PyCTRSA/blob/master/docs/Decoding_Classification_VS_CTRSA.ipynb) |

## About PyCTRSA

This work should be affilliated with [NeuroRA](https:/zitonglu1996.github.io/NeuroRA/), but it is an independent part.

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