MNEflow: Neural networks for EEG/MEG decoding and interpretation

Ivan Zubarev*, Gavriela Vranou, Lauri Parkkonen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
415 Downloads (Pure)


MNEflow is a Python package for applying deep neural networks to multichannel electroencephalograpic (EEG) and magnetoencephalographic (MEG) measurements. This software comprises Tensorflow-based implementations of several popular convolutional neural network (CNN) models for EEG–MEG data and introduces a flexible pipeline enabling easy application of the most common preprocessing, validation, and model interpretation approaches. The software aims to save time and computational resources required for applying neural networks to the analysis of EEG and MEG data.

Original languageEnglish
Article number100951
Pages (from-to)1-5
Number of pages5
Publication statusPublished - Jan 2022
MoE publication typeA1 Journal article-refereed


  • Electroencephalography
  • Machine learning
  • Magnetoencephalography
  • Neural networks
  • Tensorflow

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