项目作者: zubara

项目描述 :
Neural networks for EEG-MEG decoding with MNE-python and Tensorflow.
高级语言: Python
项目地址: git://github.com/zubara/mneflow.git
创建时间: 2019-05-16T10:03:35Z
项目社区:https://github.com/zubara/mneflow

开源协议:BSD 3-Clause "New" or "Revised" License

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MNEflow

Neural networks for EEG-MEG decoding with MNE-python and Tensorflow.

Installation

  1. pip install mneflow

Dependencies

  • tensorflow >= 2.1.0
  • mne > 0.24.0

Documentation

API reference is avalable in the Documentation.

Also check the example notebooks.

References

Zubarev I, Vranou G, Parkkonen L. MNEflow: Neural networks for EEG/MEG decoding and interpretation link

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

  1. @article{Zubarev2019AdaptiveSignals.,
  2. title = {{Adaptive neural network classifier for decoding MEG signals.}},
  3. year = {2019},
  4. journal = {NeuroImage},
  5. author = {Zubarev, Ivan and Zetter, Rasmus and Halme, Hanna-Leena and Parkkonen, Lauri},
  6. month = {5},
  7. pages = {425--434},
  8. volume = {197},
  9. url = {https://linkinghub.elsevier.com/retrieve/pii/S1053811919303544 http://www.ncbi.nlm.nih.gov/pubmed/31059799},
  10. doi = {10.1016/j.neuroimage.2019.04.068},
  11. issn = {1095-9572},
  12. pmid = {31059799},
  13. keywords = {Braincomputer interface, Convolutional neural network, Magnetoencephalography}
  14. }

for EEGNet

  1. @article{Lawhern2018,
  2. author={Vernon J Lawhern and Amelia J Solon and Nicholas R Waytowich and Stephen M Gordon and Chou P Hung and Brent J Lance},
  3. title={EEGNet: a compact convolutional neural network for EEG-based braincomputer interfaces},
  4. journal={Journal of Neural Engineering},
  5. volume={15},
  6. number={5},
  7. pages={056013},
  8. url={http://stacks.iop.org/1741-2552/15/i=5/a=056013},
  9. year={2018}
  10. }

for FBCSP-ShallowNet and Deep4

  1. @article{Schirrmeister2017DeepVisualization,
  2. title = {{Deep learning with convolutional neural networks for EEG decoding and visualization}},
  3. year = {2017},
  4. journal = {Human Brain Mapping},
  5. 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},
  6. number = {11},
  7. month = {11},
  8. pages = {5391--5420},
  9. volume = {38},
  10. url = {http://doi.wiley.com/10.1002/hbm.23730},
  11. doi = {10.1002/hbm.23730},
  12. issn = {10659471},
  13. keywords = {EEG analysis, brain, brain mapping, computer interface, electroencephalography, endtoend learning, machine interface, machine learning, model interpretability}
  14. }