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@ARTICLE{Gramfort:139493,
      author       = {Gramfort, A. and Luessi, M. and Larson, E. and Engemann, D.
                      and Strohmeier, D. and Brodbeck, C. and Goj, R. and Jas, M.
                      and Brooks, T. and Hämäläinen, M. and Parkkonen, L.},
      title        = {{MEG} and {EEG} data analysis with {MNE}-{P}ython.},
      journal      = {Frontiers in neuroscience},
      volume       = {7},
      number       = {267},
      issn         = {1662-4548},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2013-05477},
      pages        = {1-13},
      year         = {2013},
      abstract     = {Magnetoencephalography and electroencephalography (M/EEG)
                      measure the weak electromagnetic signals generated by
                      neuronal activity in the brain. Using these signals to
                      characterize and locate neural activation in the brain is a
                      challenge that requires expertise in physics, signal
                      processing, statistics, and numerical methods. As part of
                      the MNE software suite, MNE-Python is an open-source
                      software package that addresses this challenge by providing
                      state-of-the-art algorithms implemented in Python that cover
                      multiple methods of data preprocessing, source localization,
                      statistical analysis, and estimation of functional
                      connectivity between distributed brain regions. All
                      algorithms and utility functions are implemented in a
                      consistent manner with well-documented interfaces, enabling
                      users to create M/EEG data analysis pipelines by writing
                      Python scripts. Moreover, MNE-Python is tightly integrated
                      with the core Python libraries for scientific comptutation
                      (NumPy, SciPy) and visualization (matplotlib and Mayavi), as
                      well as the greater neuroimaging ecosystem in Python via the
                      Nibabel package. The code is provided under the new BSD
                      license allowing code reuse, even in commercial products.
                      Although MNE-Python has only been under heavy development
                      for a couple of years, it has rapidly evolved with expanded
                      analysis capabilities and pedagogical tutorials because
                      multiple labs have collaborated during code development to
                      help share best practices. MNE-Python also gives easy access
                      to preprocessed datasets, helping users to get started
                      quickly and facilitating reproducibility of methods by other
                      researchers. Full documentation, including dozens of
                      examples, is available at http://martinos.org/mne.},
      cin          = {INM-3},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-3-20090406},
      pnm          = {333 - Pathophysiological Mechanisms of Neurological and
                      Psychiatric Diseases (POF2-333)},
      pid          = {G:(DE-HGF)POF2-333},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000346567300262},
      pubmed       = {pmid:24431986},
      doi          = {10.3389/fnins.2013.00267},
      url          = {https://juser.fz-juelich.de/record/139493},
}