Model-driven development methodology applied to real-time MEG signal pre-processing system design
The thesis is a multidisciplinary work that uses model-based systems engineering (MBSE) for developing real-time magnetoencephalography (MEG) signal processing. In magnetoencephalography signal processing, biological artifacts in particular overtop the signal of interest by orders of magnitude and must be removed during signal processing from the measured signals to achieve a high quality reconstruction with minimized error contribution. This is a computational extremely demanding challenge as standard MEG systems include 248 and more channels in parallel. In this work, the automated real-time artifact rejection based on the recently presented method “ocular and cardiac artifact rejection for real-time analysis in MEG” (OCARTA) has been implemented by a MBSE approach and successfully verified on a Virtex-6 FPGA. The requirement-driven, model-based development methodology (RDD & MBD) provides a high-level environment and efficiently handles the complexity of computation and control systems. The applied 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 order to demonstrate the capability of the proposed approach, it was applied and further developed to the implementation of a real-time artifact rejection unit in MEG signal processing. The work of this thesis is embedded in the Jülich Research Center (Forschungszentrum Jülich GmbH, Germany) MEG-RT 2.0 project, which aims at developing an MEG real-time signal-processing device to be used as an add-on to existing MEG systems, thus enabling, for example, neuro-feedback applications. The system model of the real-time MEG signal processing chain developed here and the verified real-time artifact rejection implementation is a first and essential component of the Jülich Research Center MEG-RT 2.0 project.
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