https://github.com/mne-tools/mne-rsa
Representational Similarity Analysis on MEG and EEG data
https://github.com/mne-tools/mne-rsa
data-analysis eeg meg mne-python neuroscience
Last synced: 3 months ago
JSON representation
Representational Similarity Analysis on MEG and EEG data
- Host: GitHub
- URL: https://github.com/mne-tools/mne-rsa
- Owner: mne-tools
- License: bsd-3-clause
- Created: 2019-06-28T12:15:02.000Z (about 7 years ago)
- Default Branch: main
- Last Pushed: 2026-04-06T20:49:25.000Z (3 months ago)
- Last Synced: 2026-04-06T22:23:33.293Z (3 months ago)
- Topics: data-analysis, eeg, meg, mne-python, neuroscience
- Language: Python
- Homepage: https://mne.tools/mne-rsa
- Size: 18.4 MB
- Stars: 85
- Watchers: 1
- Forks: 16
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# MNE-RSA
[](https://github.com/mne-tools/mne-rsa/actions?query=workflow%3A%22unit+tests%22)
[](https://github.com/mne-tools/mne-rsa/actions?query=workflow%3Abuild-docs)
[](https://joss.theoj.org/papers/224328eb22eab91aaae44579fb00fdaa)
This is a Python package for performing representational similarity analysis (RSA) using [MNE-Python](https://martinos.org/mne/stable/index.html>) data structures.
The main use-case is to perform RSA using a “searchlight” approach through time and/or a
volumetric or surface source space.
Read more on RSA in the paper that introduced the technique:
Nikolaus Kriegeskorte, Marieke Mur and Peter Bandettini (2008).
Representational similarity analysis - connecting the branches of systems neuroscience.
Frontiers in Systems Neuroscience, 2(4).
[https://doi.org/10.3389/neuro.06.004.2008](https://doi.org/10.3389/neuro.06.004.2008)
## Use cases
This is what the package can do for you:
- Compute RDMs on arbitrary data
- Compute RDMs in a searchlight across:
- vertices/voxels and samples (source level)
- sensors and samples (sensor level)
- vertices/voxels only (source level)
- sensors only (sensor level)
- samples only (source and sensor level)
- Use cross-validated distance metrics when computing RDMs
- And of course: compute RSA between RDMs
Supported metrics for comparing RDMs:
- Spearman correlation (the default)
- Pearson correlation
- Kendall’s Tau-A
- Linear regression (when comparing multiple RDMs at once)
- Partial correlation (when comparing multiple RDMs at once)
## Installation
The package can be installed either through PIP: `pip install mne-rsa`
or through conda using the conda-forge channel: `conda install -c conda-forge mne-rsa`
Installing through either channel will pull in [MNE-Python](https://mne.tools) as a dependency, along with [Qt 6](https://www.qt.io), [PyVista](https://pyvista.org) and [Scikit-Learn](https://scikit-learn.org). See [`requirements.txt`](requirements.txt) for the full list of packages.
## Example usage
Basic example on the EEG “kiloword” data:
```python
import mne
import mne_rsa
# Load EEG data during which many different word stimuli were presented.
data_path = mne.datasets.kiloword.data_path(verbose=True)
epochs = mne.read_epochs(data_path / "kword_metadata-epo.fif")
# Use MNE-RSA to create model RDMs based on each stimulus property.
columns = epochs.metadata.columns[1:] # Skip the first column: WORD
model_rdms = [mne_rsa.compute_rdm(epochs.metadata[col], metric="euclidean")
for col in columns]
# Use MNE-RSA to perform RSA in a sliding window across time.
rsa_results = mne_rsa.rsa_epochs(epochs, model_rdms, temporal_radius=0.01)
# Use MNE-Python to plot the result.
mne.viz.plot_compare_evokeds(
{column: result for column, result in zip(columns, rsa_results)},
picks="rsa", legend="lower center", title="RSA result"
)
```
## Documentation
For a detailed guide on RSA analysis from start to finish on an example dataset, see the [tutorials](https://mne.tools/mne-rsa/stable/auto_examples/tutorials/index.html).
For quick guides on how to do specific things, see the [examples](https://mne.tools/mne-rsa/stable/auto_examples/index.html).
Finally, there is the [API reference](https://mne.tools/mne-rsa/stable/api.html) documentation.
## Integration with other packages
The main purpose of this package is to perform RSA analysis on MEG data.
Hence, integration functions with [MNE-Python](https://mne.tools) are provided.
However, there is also some integration with [nipy](https://nipy.org) for fMRI that should well in a [nilearn](https://nilearn.github.io) setup.
## Support
This free software comes without any form of official support.
However, if you think you have encountered a bug or have a particularly great idea for improvements, please open an [issue on Github](https://github.com/mne-tools/mne-rsa/issues).
For questions and help with your analysis, you are welcome to post on the [MNE forum](https://mne.discourse.group/).
## Contributing
Development of the package happens on [Github](https://github.com/mne-tools/mne-rsa) under the umbrella of MNE-tools.
Everyone is welcome to raise [issues](https://github.com/mne-tools/mne-rsa/issues) or contribute [pull requests](https://github.com/mne-tools/mne-rsa/pulls) as long as they abide by our [Code of Conduct](https://github.com/mne-tools/.github/blob/main/CODE_OF_CONDUCT.md).
For more information about the ways in which one can contribute, see the [Contributing guide of MNE-Python](https://mne.tools/stable/development/contributing.html), which by and large applies to this project as well.
Here is how to install the additional required packages for developing MNE-RSA and set up the package in development mode:
```bash
git clone git@github.com:mne-tools/mne-rsa.git
cd mne-rsa
pip install -r requirements-dev.txt
pip install -e .
```
To run the test suite, execute `pytest` in the main `mne-rsa` folder.
To build the documentation, execute `make html` in the `mne-rsa/doc` folder (or on
Windows: `sphinx-build . _build/html`).
## Citation
If you end up using this package for the data analysis that is part of a scientific
article, please cite:
Marijn van Vliet, Stefan Appelhoff, Takao Shimizu, Egor Eremin, Annika Hultén, Yuanfang Zhao, and Richard Höchenberger, (2025). MNE-RSA: Representational Similarity Analysis on EEG, MEG and fMRI data. Journal of Open Source Software, 10(116), 9148, https://doi.org/10.21105/joss.09148