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@ARTICLE{Chen:878342,
      author       = {Chen, Tao and Schiek, Michael and Dammers, Jürgen and
                      Shah, N. Jon and van Waasen, Stefan},
      title        = {{R}equirement-driven model-based development methodology
                      applied to the design of a real-time {MEG} data processing
                      unit},
      journal      = {Software and systems modeling},
      volume       = {19},
      issn         = {1619-1374},
      address      = {New York, NY},
      publisher    = {Springer},
      reportid     = {FZJ-2020-02791},
      pages        = {1567–1587},
      year         = {2020},
      abstract     = {The paper describes a multidisciplinary work that uses a
                      model-based systems engineering method for developing
                      real-time magnetoencephalography (MEG) signal processing. We
                      introduce a requirement-driven, model-based development
                      methodology (RDD and MBD) to provide a high-level
                      environment and efficiently handle the complexity of
                      computation and control systems. The proposed development
                      methodology focuses on the use of System Modeling Language
                      to define high-level model-based design descriptions for
                      later implementation in heterogeneous hardware/software
                      systems. The proposed approach was applied to the
                      implementation of a real-time artifact rejection unit in MEG
                      signal processing and demonstrated high efficiency in
                      designing complex high-performance embedded systems. In MEG
                      signal processing, biological artifacts in particular have a
                      signal strength that overtop the signal of interest by
                      orders of magnitude and must be removed from the measurement
                      to achieve high-quality source reconstructions with minimal
                      error contributions. However, many existing brain–computer
                      interface studies overlook real-time artifact removal
                      because of the demanding computational process. In this
                      work, an automated real-time artifact rejection method is
                      introduced, which is based on the recently presented method
                      “ocular and cardiac artifact rejection for real-time
                      analysis in MEG” (OCARTA). The method has been implemented
                      using the RDD and MBD approach and successfully verified on
                      a Virtex-6 field-programmable gate array.},
      cin          = {INM-4 / ZEA-2 / INM-11 / JARA-BRAIN},
      ddc          = {004},
      cid          = {I:(DE-Juel1)INM-4-20090406 / I:(DE-Juel1)ZEA-2-20090406 /
                      I:(DE-Juel1)INM-11-20170113 / I:(DE-Juel1)VDB1046},
      pnm          = {573 - Neuroimaging (POF3-573)},
      pid          = {G:(DE-HGF)POF3-573},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000531132800001},
      doi          = {10.1007/s10270-020-00797-3},
      url          = {https://juser.fz-juelich.de/record/878342},
}