Abstrakti
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)
Alkuperäiskieli | Englanti |
---|---|
Tuotoksen media | Online |
Tila | Julkaistu - 21 huhtik. 2020 |
OKM-julkaisutyyppi | I2 Tieto- ja viestintätekniset sovellukset |