MNE-RSA: Representational similarity analysis using MNE-Python datastructures

Research output: Artistic and non-textual formSoftwareScientific

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)
Original languageEnglish
Media of outputOnline
Publication statusPublished - 21 Apr 2020
MoE publication typeI2 ICT software

Keywords

  • python
  • rsa
  • data analysis
  • open source

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