paper.bib

@article{Gorgolewski2016,
  title    = "The brain imaging data structure, a format for organizing and
              describing outputs of neuroimaging experiments",
  author   = {Gorgolewski, Krzysztof J and
              Auer, Tibor and
              Calhoun, Vince D and
              Craddock, R Cameron and
              Das, Samir and
              Duff, Eugene P and
              Flandin, Guillaume and
              Ghosh, Satrajit S and
              Glatard, Tristan and
              Halchenko, Yaroslav O and
              Handwerker, Daniel A and
              Hanke, Michael and
              Keator, David and
              Li, Xiangrui and
              Michael, Zachary and
              Maumet, Camille and
              Nichols, B Nolan and
              Nichols, Thomas E and
              Pellman, John and
              Poline, Jean-Baptiste and
              Rokem, Ariel and
              Schaefer, Gunnar and
              Sochat, Vanessa and
              Triplett, William and
              Turner, Jessica A and
              Varoquaux, Ga{\"e}l and
              Poldrack, Russell A},
  abstract = "The development of magnetic resonance imaging (MRI) techniques
              has defined modern neuroimaging. Since its inception, tens of
              thousands of studies using techniques such as functional MRI and
              diffusion weighted imaging have allowed for the non-invasive
              study of the brain. Despite the fact that MRI is routinely used
              to obtain data for neuroscience research, there has been no
              widely adopted standard for organizing and describing the data
              collected in an imaging experiment. This renders sharing and
              reusing data (within or between labs) difficult if not impossible
              and unnecessarily complicates the application of automatic
              pipelines and quality assurance protocols. To solve this problem,
              we have developed the Brain Imaging Data Structure (BIDS), a
              standard for organizing and describing MRI datasets. The BIDS
              standard uses file formats compatible with existing software,
              unifies the majority of practices already common in the field,
              and captures the metadata necessary for most common data
              processing operations.",
  journal  = "Scientific Data",
  volume   =  3,
  pages    = "160044",
  month    =  jun,
  year     =  2016,
  doi      =  {10.1038/sdata.2016.44},
  language = "en"
}

@article{Agramfort2013,
  title    = "MEG and EEG data analysis with MNE-Python",
  author   = {Gramfort, Alexandre and
              Luessi, Martin and
              Larson, Eric and
              Engemann, Denis A and
              Strohmeier, Daniel and
              Brodbeck, Christian and
              Goj, Roman and
              Jas, Mainak and
              Brooks, Teon and
              Parkkonen, Lauri and
              Hämäläinen, Matti},
  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.",
  journal  = "Frontiers in Neuroscience",
  volume   =  7,
  pages    = "267",
  month    =  dec,
  year     =  2013,
  doi      =  {10.3389/fnins.2013.00267},
  language = "en"
}

@article{Niso2018,
  title    = "MEG-BIDS, the brain imaging data structure extended to
              magnetoencephalography",
  author   = {Niso, Guiomar and
              Gorgolewski, Krzysztof J and
              Bock, Elizabeth and
              Brooks, Teon L and
              Flandin, Guillaume and
              Gramfort, Alexandre and
              Henson, Richard N and
              Jas, Mainak and
              Litvak, Vladimir and
              Moreau, Jeremy T and
              Oostenveld, Robert and
              Schoffelen, Jan-Mathijs and
              Tadel, Francois and
              Wexler, Joseph and
              Baillet, Sylvain},
  abstract = "We present a significant extension of the Brain Imaging Data
              Structure (BIDS) to support the specific aspects of
              magnetoencephalography (MEG) data. MEG measures brain activity
              with millisecond temporal resolution and unique source imaging
              capabilities. So far, BIDS was a solution to organise magnetic
              resonance imaging (MRI) data. The nature and acquisition
              parameters of MRI and MEG data are strongly dissimilar. Although
              there is no standard data format for MEG, we propose MEG-BIDS as
              a principled solution to store, organise, process and share the
              multidimensional data volumes produced by the modality. The
              standard also includes well-defined metadata, to facilitate
              future data harmonisation and sharing efforts. This responds to
              unmet needs from the multimodal neuroimaging community and paves
              the way to further integration of other techniques in
              electrophysiology. MEG-BIDS builds on MRI-BIDS, extending BIDS to
              a multimodal data structure. We feature several data-analytics
              software that have adopted MEG-BIDS, and a diverse sample of open
              MEG-BIDS data resources available to everyone.",
  journal  = "Scientific Data",
  volume   =  5,
  pages    = "180110",
  month    =  jun,
  year     =  2018,
  doi      =  {10.1038/sdata.2018.110},
  language = "en"
}

@article{Pernet2019,
  title    = "EEG-BIDS, an extension to the brain imaging data structure for
              electroencephalography",
  author   = {Pernet, Cyril R and
              Appelhoff, Stefan and
              Gorgolewski, Krzysztof J and
              Flandin, Guillaume and
              Phillips, Christophe and
              Delorme, Arnaud and
              Oostenveld, Robert},
  abstract = "The Brain Imaging Data Structure (BIDS) project is a rapidly
              evolving effort in the human brain imaging research community to
              create standards allowing researchers to readily organize and
              share study data within and between laboratories. Here we present
              an extension to BIDS for electroencephalography (EEG) data,
              EEG-BIDS, along with tools and references to a series of public
              EEG datasets organized using this new standard.",
  journal  = "Scientific Data",
  volume   =  6,
  pages    = "103",
  month    =  jun,
  year     =  2019,
  doi      =  {10.1038/s41597-019-0104-8},
  language = "en"
}

@article{Holdgraf2019,
  title    = "iEEG-BIDS, extending the Brain Imaging Data Structure to human
              intracranial electrophysiology",
  author   = {Holdgraf, Christopher and
              Appelhoff, Stefan and
              Bickel, Stephan and
              Bouchard, Kristofer and
              D'Ambrosio, Sasha and
              David, Oliver and
              Devinsky, Orrin and
              Dichter, Benjamin and
              Flinker, Adeen and
              Foster, Brett L and
              Gorgolewski, Krzysztof J and
              Groen, Iris and
              Groppe, David and
              Gunduz, Aysegul and
              Hamilton, Liberty and
              Honey, Christpher J and
              Jas, Mainak and
              Knight, Robert and
              Lachaux, Jean-Philippe and
              Lau, Jonathan C and
              Lee-Messer, Christopher and
              Lundstrom, Brian N and
              Miller, Kai J and
              Ojemann, Jeffrey G and
              Oostenveld, Robert and
              Petridou, Natalia and
              Piantoni, Gio and
              Pigorini, Andrea and
              Pouratian, Nader and
              Ramsey, Nick F and
              Stolk, Arjen and
              Swann, Nicole C and
              Tadel, Francois and
              Voytek, Bradley and
              Wandell, Brian A and
              Winawer, Jonathan and
              Whitaker, Kristie and
              Zehl, Lyuba and
              Hermes, Dora},
  abstract = "The Brain Imaging Data Structure (BIDS) is a community-driven
              specification for organizing neuroscience data and metadata with
              the aim to make datasets more transparent, reusable, and
              reproducible. Intracranial electroencephalography (iEEG) data
              offer a unique combination of high spatial and temporal
              resolution measurements of the living human brain. To improve
              internal (re)use and external sharing of these unique data, we
              present a specification for storing and sharing iEEG data:
              iEEG-BIDS.",
  journal  = "Scientific Data",
  volume   =  6,
  pages    = "102",
  month    =  jun,
  year     =  2019,
  doi      =  {10.1038/s41597-019-0105-7},
  language = "en"
}

@article{Appelhoff2019,
  doi = {10.21105/joss.01896},
  url = {https://doi.org/10.21105/joss.01896},
  year = 2019,
  publisher = {The Open Journal},
  volume = 4,
  number = 44,
  pages = 1896,
  author = {Appelhoff, Stefan and 
            Sanderson, Matthew and
            Brooks, Teon L. and 
            van Vliet, Marijn and
            Quentin, Romain and
            Holdgraf, Chris and
            Chaumon, Maximilien and
            Mikulan, Ezequiel and
            Tavabi, Kambiz and
            Höchenberger, Richard and
            Welke, Dominik and
            Brunner, Clemens and
            Rockhill, Alexander P. and
            Larson, Eric and
            Gramfort, Alexandre and
            Jas, Mainak},
  title = {MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis},
  journal = {Journal of Open Source Software}
}