% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Chen:878546,
      author       = {Chen, Tao and Suslov, Sergey and Schiek, Michael and Shah,
                      N. Jon and Waasen, Stefan van and Dammers, Jurgen},
      title        = {{M}odel-{D}riven {D}evelopment {M}ethodology {A}pplied to
                      {R}eal-{T}ime {MEG} {S}ignal {P}reprocessing {S}ystem
                      {D}esign},
      publisher    = {IEEE},
      reportid     = {FZJ-2020-02905},
      pages        = {28-33},
      year         = {2017},
      comment      = {2017 European Modelling Symposium (EMS) : [Proceedings] -
                      IEEE, 2017. - ISBN 978-1-5386-1410-5 -
                      doi:10.1109/EMS.2017.16},
      booktitle     = {2017 European Modelling Symposium
                       (EMS) : [Proceedings] - IEEE, 2017. -
                       ISBN 978-1-5386-1410-5 -
                       doi:10.1109/EMS.2017.16},
      abstract     = {Model-based system engineering (MBSE) provides a high-level
                      environment and efficiently handles the ever-rising
                      complexity of computation and control systems. We introduce
                      a requirement-driven, model-based development methodology
                      (RDD $\&$ MBD) for real-time computation systems based on
                      vendor neutral specifications. The proposed development
                      methodology focuses on the use of Systems Modeling Language
                      (SysML) to define high-level model-based design descriptions
                      for later implementation in heterogeneous hardware/software
                      systems. In magnetoencephalography (MEG) data processing,
                      biological artifacts in particular overtop the signal of
                      interest by orders of magnitude and must be removed from the
                      measured signals to avoid reconstruction errors. However,
                      many real-time brain computer interface (BCI) approaches
                      neglect real-time artifact removal as it is computationally
                      demanding. Therefore, we applied the RDD $\&$ MBD approach
                      to the design of a system-on-chip (SoC), capable of
                      performing real-time artifact rejection, based on the
                      recently presented method 'Ocular and Cardiac Artifact
                      rejection for Real-Time Analysis in MEG' (OCARTA).},
      month         = {Nov},
      date          = {2017-11-20},
      organization  = {2017 UKSim-AMSS 11th European
                       Modelling Symposium (EMS), Manchester
                       (United Kingdom), 20 Nov 2017 - 21 Nov
                       2017},
      cin          = {INM-4 / INM-11 / JARA-BRAIN / ZEA-2},
      cid          = {I:(DE-Juel1)INM-4-20090406 / I:(DE-Juel1)INM-11-20170113 /
                      I:(DE-Juel1)VDB1046 / I:(DE-Juel1)ZEA-2-20090406},
      pnm          = {573 - Neuroimaging (POF3-573)},
      pid          = {G:(DE-HGF)POF3-573},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      UT           = {WOS:000434813800005},
      doi          = {10.1109/EMS.2017.16},
      url          = {https://juser.fz-juelich.de/record/878546},
}