Metadata-Version: 2.1
Name: mneflow
Version: 0.4.0
Summary: Neural networks for MEG and EEG data
Home-page: https://github.com/zubara/mneflow
Maintainer: Ivan Zubarev
Maintainer-email: ivan.zubarev@aalto.fi
License: BSD-3
Download-URL: https://github.com/zubara/mneflow/archive/master.zip
Platform: any
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Description-Content-Type: text/x-rst
License-File: LICENSE.md

MNEflow

=======

MNEflow provides a convenient way to apply neural networks implemmented in Tensorflow to EEG/MEG data. 





Installation

============

>>> pip install mneflow



Dependencies

============

* tensorflow > 2.1.0

* mne < 0.19





Examples

========

* Data Import and `basic MNEflow pipeline <https://github.com/zubara/mneflow/blob/master/examples/mneflow_example_tf2.ipynb>`_.

* `Regression, adjusting hyperparameters, and corss-validation <https://github.com/zubara/mneflow/blob/master/examples/regression_example.ipynb>`_.

* `Custom network design <https://github.com/zubara/mneflow/blob/master/examples/own_graph_example.ipynb>`_.







References 

==========

When using the implemented models please cite: 



*for LF-CNN or VAR-CNN*



Zubarev I, Zetter R, Halme HL, Parkkonen L. Adaptive neural network classifier for decoding MEG signals. Neuroimage. 2019 May 4;197:425-434. 

`[link] <https://www.sciencedirect.com/science/article/pii/S1053811919303544?via%3Dihub>`_::



	``@article{Zubarev2019AdaptiveSignals.,

	    title = {{Adaptive neural network classifier for decoding MEG signals.}},

	    year = {2019},

	    journal = {NeuroImage},

	    author = {Zubarev, Ivan and Zetter, Rasmus and Halme, Hanna-Leena and Parkkonen, Lauri},

	    month = {5},

	    pages = {425--434},

	    volume = {197},

	    url = {https://linkinghub.elsevier.com/retrieve/pii/S1053811919303544 http://www.ncbi.nlm.nih.gov/pubmed/31059799},

	    doi = {10.1016/j.neuroimage.2019.04.068},

	    issn = {1095-9572},

	    pmid = {31059799},

	    keywords = {Brain–computer interface, Convolutional neural network, Magnetoencephalography}}``





*for EEGNet*::



	``@article{Lawhern2018,

	  author={Vernon J Lawhern and Amelia J Solon and Nicholas R Waytowich and Stephen M Gordon and Chou P Hung and Brent J Lance},

	  title={EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces},

	  journal={Journal of Neural Engineering},

	  volume={15},

	  number={5},

	  pages={056013},

	  url={http://stacks.iop.org/1741-2552/15/i=5/a=056013},

	  year={2018}}``





*for FBCSP-ShallowNet and Deep4*::



	``@article{Schirrmeister2017DeepVisualization,

	    title = {{Deep learning with convolutional neural networks for EEG decoding and visualization}},

	    year = {2017},

	    journal = {Human Brain Mapping},

	    author = {Schirrmeister, Robin Tibor and Springenberg, Jost Tobias and Fiederer, Lukas Dominique Josef and Glasstetter, Martin and Eggensperger, Katharina and 	Tangermann, Michael and Hutter, Frank and Burgard, Wolfram and Ball, Tonio},

	    number = {11},

	    month = {11},

	    pages = {5391--5420},

	    volume = {38},

	    url = {http://doi.wiley.com/10.1002/hbm.23730},

	    doi = {10.1002/hbm.23730},

	    issn = {10659471},

	    keywords = {EEG analysis, brain, brain mapping, computer interface, electroencephalography, end‐to‐end learning, machine interface, machine learning, model interpretability}

	}``







