Partial least squares correlation (PLSC) for M/EEG
mne-plsc is a library for partial least squares correlation (PLSC) analysis of M/EEG data in Python, integrated with the MNE-Python library. The basic computations are performed by the pyplsc library, and the documentation of that library contains some background on the PLSC technique.
Installation
mne-plsc can be installed from the Python Package Index with
pip install mne-plsc
Quickstart
The main functions for model fitting are fit_mc, fit_beh, and fit_within_beh. These return objects whose methods can be used for permutation testing, cluster analysis, and visualization. The typical workflow would be:
1. Fit and visualize model
Perform the initial decomposition and check the patterns of saliences.
from mne_plsc import fit_mc
mod = fit_mc(epochs, condition)
mod.plot_lv(0)
2. Permutation testing
Evaluate which latent variables are significant.
mod.permute(1000)
print(model.summary())
3. Cluster analysis
Perform bootstrap resampling to estimate brain salience z-scores, then cluster strong saliences (e.g., $|z| > 2$).
mod.bootstrap(1000)
mod.cluster(threshold=2)
4. Visualize cluster(s)
Examine the temporal/spectral/spatial distribution of the major clusters for a given set of brain saliences.
mod.plot_cluster_sizes(lv_idx=0)
mod.plot_cluster(lv_idx=0, cluster_idx=0)
5. Extract and export data in cluster(s)
For further analysis, we can extract data at cluster peaks (or averages within clusters) and export to a spreadsheet.
df = mod.get_cluster_data(lv_idx=[0, 1, 2], cluster_idx=[0, 1])
df.to_csv('cluster-data.csv')
See the examples in the documentation for more details.
Release files for mne-plsc 0.0.34
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| mne_plsc-0.0.34.tar.gz | 29.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| mne_plsc-0.0.34-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 58.1 kB
Release files / mne_plsc-0.0.34.tar.gz
| Download URL | mne_plsc-0.0.34.tar.gz |
|---|---|
| Size | 29.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
bb5d08c7e535bbfca70f3a75dcf7ca26a7a4bebec3d0f87a7e47cc1d4883ff29
|
|
BLAKE2b-256 checksum How to use checksums |
f215c1798690c3b09323a86dc8a63554403460ad48cf96c92bafd379fc4b67f7
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Release files / mne_plsc-0.0.34-py3-none-any.whl
| Download URL | mne_plsc-0.0.34-py3-none-any.whl |
|---|---|
| Size | 28.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
7b929cf6a50a060160ac74e5cab3d51725e59aca1f4f2ce84b9ba5657fa8d167
|
|
BLAKE2b-256 checksum How to use checksums |
75a2ba4bf68617e34dcb702ba2b4f673e18fc2dac7af9c3bdf801783abd7a6cd
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
