Solves the M/EEG inverse problem using artificial neural networks with Python 3 and the MNE library.
Project description
esinet: Electric source imaging using artificial neural networks (ANNs)
esinet let's you solve the EEG inverse problem using ANNs. It currently supports one architecture: A fully connected neural network which is trained on single time instances of M/EEG data. This model was described in our paper.
Neural network design was created here
Dependencies:
- Python >= 3.8.3
- mne 0.22.0
- Follow the installation guide
- Tensorflow>=2.4.1
- Follow the installation guide
- Colorednoise
- joblib
- pyvista>=0.24
- pyvistaqt>=0.2.0
- tqdm
Installation from PyPi
Use pip to install esinet and all its dependencies from PyPi:
pip install esinet
Quick start
The following code demonstrates how to use this package:
from esinet import Simulation, Net
# Simulate M/EEG data
settings = dict(duration_of_trial=0.)
sim = Simulation(fwd, info, settings=settings)
sim.simulate(n_samples=10000)
# Train neural network on the simulated data
net = Net(fwd)
net.fit(sim)
# Perform predictions on your data
stc = net.predict(epochs)
First steps
Check out one of the tutorials to learn how to use the package:
- Tutorial 1: The fastest way to get started with the fully-connected ANN. This tutorial can be used as an entry point. If you want to dig deeper you should have a look at the next tutorials, too!
- Tutorial 2: Use esinet with low-level functions that allow for more control over your parameters with respect to simulations and training of the neural network.
- Tutorial 3: A demonstration of simulation parameters and how they affect the model performance.
Feedback
Please leave your feedback and bug reports at lukas_hecker@web.de.
Literature
Please cite us with this publication:
@ARTICLE{10.3389/fnins.2021.569918,
AUTHOR={Hecker, Lukas and Rupprecht, Rebekka and Tebartz Van Elst, Ludger and Kornmeier, Jürgen},
TITLE={ConvDip: A Convolutional Neural Network for Better EEG Source Imaging},
JOURNAL={Frontiers in Neuroscience},
VOLUME={15},
PAGES={533},
YEAR={2021},
URL={https://www.frontiersin.org/article/10.3389/fnins.2021.569918},
DOI={10.3389/fnins.2021.569918},
ISSN={1662-453X}
}
Troubleshooting
- Having problems with the installation? Check the package requirements
Notes on current version
- This version 0.1.0 is fully compatible with the mne-python package. This meant that I had to change the whole API to match the mne-python API. Please have a look at the new tutorials in order to get familiar with the new code structure or revert back to an earlier version. Check the changelog for a version list.
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