This repository contains the source code for the Local AUto-Regressive Average (LAURA) inverse solution as described by de Peralta Menendez et al. (2001, 2004). The code is based on mne-python, a powerful EEG library for python.
Personally, I think this linear inverse solution finds the neural sources underlying M/EEG measurements with great success and is a valuable option among other inverse solutions such as minimum norm estimates and (e)LORETA.
That's it!
Use pip to install laura and all its dependencies from PyPi:
pip install laura
The following code demonstrates how to use this package:
from laura import compute_laura
stc = compute_laura(evoked, forward)
stc.plot()
, where evoked is an instance of mne.Evoked and forward is an instance of mne.Forward. For further explanation on mne and its objects please refer to the mne website.
For a more comprehensive tutorial hop over to this notebook!
Please leave your feedback and bug reports at lukas_hecker@web.de.
Please cite the authors of the LAURA inverse solution appropriately:
[1] Menendez, R. G. D. P., Andino, S. G., Lantz, G., Michel, C. M., & Landis, T. (2001). Noninvasive localization of electromagnetic epileptic activity. I. Method descriptions and simulations. Brain topography, 14(2), 131-137.
[2] de Peralta Menendez, R. G., Murray, M. M., Michel, C. M., Martuzzi, R., & Andino, S. L. G. (2004). Electrical neuroimaging based on biophysical constraints. Neuroimage, 21(2), 527-539.
I would be happy if you would cite this package, too:
LAURA was calculated using the laura python package available at https://github.com/LukeTheHecker/laura.
The current implementation is limited to:
- fixed dipole orientations
- time-domain EEG data
Feel free to modify the code and start a pull request!
- Having problems with the installation? Check the package requirements.