arthurdehgan/meegnet

CAMCAN dataset analysis

Welcome to MEEGNet!

This is a suite of tools made available for neuroscience researchers to use a variety of M/EEG neural network architectures on their data.

• Source: https://github.com/arthurdehgan/meegnet
• Bug Reports: https://github.com/arthurdehgan/meegnet/issues

MeegNet Architecture

Other Available Architectures

The package currently supports the following architectures:

• LF-CNN
• VAR-CNN
• EEGNet

Documentation

For a full documentation of the package as well as tutorials on how to use the library, click the Readthedocs link.

Features

Work in Progress

Tutorials

Jupyter Notebook tutorials:

Prepare your data by following the instructions here

Learn the basics of how to train and evaluate using a pre-made network here

Alternatives

Maybe this package doesn’t suit your needs, in which case we can recommend similar packages with similar goals:

• https://mneflow.readthedocs.io/en/latest/
• https://braindecode.org/stable/index.html

References

MEEGNet

Work in Progress

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

@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}
}

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}
}