Requirement-driven model-based development methodology applied to the design of a real-time MEG data processing unit
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Requirement-driven model-based development methodology applied to the design of a real-time MEG data processing unit Tao Chen1,2 · Michael Schiek1 · Jürgen Dammers2 · N. Jon Shah2,3,4 · Stefan van Waasen1,5 Received: 27 May 2019 / Revised: 7 February 2020 / Accepted: 28 March 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The paper describes a multidisciplinary work that uses a model-based systems engineering method for developing realtime 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 highlevel 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. Keywords MBSE · SysML · Real-time systems · MEG · Artifact rejection · Neurofeedback
1 Introduction In recent years, semiconductor technology has advanced continually; the scale of heterogeneous systems has been increasing dramatically and very different disciplines are involved [1]. However, an interconnected system of systems (SoS) as a whole is much greater than the sum of the individCommunicated by Juergen Dingel.
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Tao Chen [email protected]
1
Central Institute of Electronic Systems (ZEA-2), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
2
Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
3
Department of Neurology, Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany
4
JARA–BRAIN–Translational Medicine, RWTH Aachen University, 52074 Aachen, Germany
5
Communication Systems, Faculty of Engineering, University of Duisburg-Essen, 47057 Duisburg, Germany
ual subsystems [2]. The interconnectivity and data transfer among the constituent systems can be extremely complex and hard to control [3]. Systems engineering (SE) is an interdisciplinary approach for handling the complex
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