Journal Article

FZJ-2020-02791

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Requirement-driven model-based development methodology applied to the design of a real-time MEG data processing unit

Chen, T. (Corresponding author) ; Schiek, M.FZJ* ; Dammers, J.FZJ* ; Shah, N. J.FZJ* ; van Waasen, S.FZJ*

2020
Springer New York, NY

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Please use a persistent id in citations: http://hdl.handle.net/2128/25868  doi:10.1007/s10270-020-00797-3

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.

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Contributing Institute(s):

1. Physik der Medizinischen Bildgebung (INM-4)
2. Zentralinstitut für Elektronik (ZEA-2)
3. Jara-Institut Quantum Information (INM-11)
4. Jülich-Aachen Research Alliance - Translational Brain Medicine (JARA-BRAIN)

Research Program(s):

1. 573 - Neuroimaging (POF3-573) (POF3-573)

Appears in the scientific report 2020

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Database coverage:
; ; Clarivate Analytics Master Journal List ; Current Contents - Engineering, Computing and Technology ; DEAL Springer ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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