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Contextual Minimum-Norm Estimates (CMNE): A Deep Learning Method for Source Estimation in Neuronal Networks

For more information on CMNE, please read the following papers:

Dinh C, Samuelsson JGW*, Hunold A, Hämäläinen MS, Khan S. Contextual MEG and EEG Source Estimates Using Spatiotemporal LSTM Networks. Front. Neurosci 2021;15:119-134; doi: https://doi.org/10.3389/fnins.2021.552666

Dinh C, Samuelsson JGW*, Hunold A, Hämäläinen MS, Khan S. Contextual Minimum-Norm Estimates (CMNE): A Deep Learning Method for Source Estimation in Neuronal Networks. arXiv:1909.02636; doi: https://doi.org/10.48550/arXiv.1909.02636

Installation

To install the latest stable version of CMNE, you can use pip in a terminal:

pip install -U cmne

Usage of the Docker Container

Build the docker image with

docker build -t brain-link/cmne:v0.01 .

and run it with

docker run -ti -v <YOUR DATA DIR>:/workspace/data -v <YOUR CMNE RESULTS DIR>:/workspace/results -v <YOUR CMNE GIT DIR>:/workspace/cmne --name CMNE brain-link/cmne:v0.01

It is convinient to install CMNE for development directly from the local repository. Change the directory to '/workspace/cmne' in the CLI of the Docker Container and run

pip install -e .

Licensing

CMNE is MIT-licensed:

Copyright (c) 2017-2022, authors of CMNE. All rights reserved.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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Contextual Minimum-Norm Estimates (CMNE): A Deep Learning Method for Source Estimation in Neuronal Networks

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