## Abstract

This is a Python package for performing representational similarity analysis (RSA) using MNE-Python data structures. The RSA is computed using a “searchlight” approach.

This is what the package can do for you:

- Compute DSMs on arbitrary data

- Compute DSMs in a searchlight across:

- vertices and samples (source level)

- sensors and samples (sensor level)

- vertices only (source level)

- sensors only (sensor level)

- samples only (source and sensor level)

- Use cross-validated distance metrics when computing DSMs

- And of course: compute RSA between DSMs

This is what it cannot do (yet) for you:

- Compute DSMs in a searchlight across voxels (volume level)

Supported metrics for comparing DSMs:- Spearman correlation (the default)

- Pearson correlation

- Kendall’s Tau-A

- Linear regression (when comparing multiple DSMs at once)

- Partial correlation (when comparing multiple DSMs at once)

This is what the package can do for you:

- Compute DSMs on arbitrary data

- Compute DSMs in a searchlight across:

- vertices and samples (source level)

- sensors and samples (sensor level)

- vertices only (source level)

- sensors only (sensor level)

- samples only (source and sensor level)

- Use cross-validated distance metrics when computing DSMs

- And of course: compute RSA between DSMs

This is what it cannot do (yet) for you:

- Compute DSMs in a searchlight across voxels (volume level)

Supported metrics for comparing DSMs:- Spearman correlation (the default)

- Pearson correlation

- Kendall’s Tau-A

- Linear regression (when comparing multiple DSMs at once)

- Partial correlation (when comparing multiple DSMs at once)

Original language | English |
---|---|

Media of output | Online |

Publication status | Published - 21 Apr 2020 |

MoE publication type | I2 ICT software |

## Keywords

- python
- rsa
- data analysis
- open source