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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References
Weilkiens, T.: Systems Engineering with SysML/UML: Modeling, Analysis, Design. Elsevier, Amsterdam (2011)
Office of the Deputy under Secretary of Defense for Acquisition and Technology, Systems and Software Engineering. Systems Engineering Guide for Systems of Systems, Version 1.0. Washington, DC: ODUSD (A&T) SSE (2008)
Chen, T., et al.: Model-driven development methodology applied to real-time MEG signal preprocessing system design. In: 2017 European Modelling Symposium (EMS). IEEE (2017)
Suslov, S.: Parallelisation Potential of Image Segmentation in Hierarchical Island Structures on Hardware-Accelerated Platforms in Real-Time Applications. Forschungszentrum Jülich, Jülich (2013)
Suslov, S.: Presentation: SysML for computing controlling system development. https://www.fz-juelich.de/SharedDocs/Downloads/ZEA/ZEA-2/DE/SysML.pdf?__blob=publicationFile (2017)
Biomagnetic Technologies Inc., “MAGNES 2500 WH-X and 3600 WH Hardware Reference Manual,” BTi San Diego, California, USA, 2006.
Dammers, J., et al.: Integration of amplitude and phase statistics for complete artifact removal in independent components of neuromagnetic recordings. IEEE Trans. Biomed. Eng. 55(10), 2353–2362 (2008)
Breuer, L., et al.: Ocular and cardiac artifact rejection for real-time analysis in MEG. J. Neurosci. Methods 233, 105–114 (2014)
Sudre, G., et al.: rtMEG: a real-time software interface for magnetoencephalography. Comput. Intell. Neurosci. 2011, 11 (2011)
Breuer, L.: Identification of Neuromagnetic Responses for Real-Time Analysis in Magnetoencephalography. RWTH Aachen University, Aachen (2015)
Rongen, H., Hadamschek, V., Schiek, M.: Real time data acquisition and online signal processing for magnetoencephalography. In: Real Time Conference, 2005. 14th IEEE-NPSS. IEEE (2005)
Buch, E., et al.: Think to move: a neuromagnetic brain–computer interface (BCI) system for chronic stroke. Stroke 39(3), 910–917 (2008)
Mellinger, J., et al.: An MEG-based brain–computer interface (BCI). Neuroimage 36(3), 581–593 (2007)
Esch, L., et al.: MNE Scan: Software for real-time processing of electrophysiological data. J. Neurosci. Methods 303, 55–67 (2018)
Dinh, C., et al.: Real-Time MEG Source Localization Using Regional Clustering. Brain Topogr. 28(6), 771–784 (2015)
Rosenberg, D., Mancerella, S.: Embedded systems development using SysML: an illustrated example using enterprise architect. Sparx Systems Pty Ltd and ICONIX, pp. 4–14 (2010)
Hämäläinen, M., et al.: Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev. Mod. Phys. 65(2), 413 (1993)
Dale, C.L., et al.: Timing is everything: neural response dynamics during syllable processing and its relation to higher-order cognition in schizophrenia and healthy comparison subjects. Int. J. Psychophysiol. 75(2), 183–193 (2010)
Lauer, R.T., et al.: Applications of cortical signals to neuroprosthetic control: a critical review. IEEE Trans. Rehabil. Eng. 8(2), 205–208 (2000)
Object Management Group: OMG System modeling Language (OMG SysML), v1.3. https://www.omg.org/spec/SysML/1.3/ (2012)
Zamrini, E., Maestu, F., Pekkonen, E., Funke, M., Makela, J., Riley, M., et al.: Magnetoencephalography as a putative biomarker for Alzheimer’s disease. Int J Alzheimers Dis 2011, 280289 (2011)
Friedenthal, S., Moore, A., Steiner, R.: A Practical Guide to SysML: The System Modeling Language. Morgan Kaufmann, Burlington (2014)
Delligatti, L.: SysML Distilled: A Brief Guide to the System Modeling Language. Addison-Wesley, Boston (2013)
Sattar, F., Charayaphan, C.: Low-cost design and implementation of an ICA-based blind source separation algorithm. In: 15th Annual IEEE International ASIC/SOC Conference, 2002. IEEE (2002)
Li, Z., Lin, Q.: FPGA implementation of Infomax BSS algorithm with fixed-point number representation. In: International Conference on Neural Networks and Brain, 2005. ICNN&B'05, vol. 2. IEEE (2005)
Wang, J.-C., et al.: VLSI design for convolutive blind source separation. IEEE Trans. Circuits Syst. 63(2), 196–200 (2016)
Gupta, S., et al.: Deep learning with limited numerical precision. In: International Conference on Machine Learning (2015)
Paredis, C.J.J., et al.: An overview of the SysML-Modelica transformation specification. In: INCOSE International Symposium (2010)
Model-based design homepage: https://ww2.mathworks.cn/solutions/model-based-design.html (2019)
Karamizadeh, S., et al.: An overview of principal component analysis. J. Signal Inf. Process. 4(3), 173 (2013)
Shahrouzi, S.N., Perera, D.G.: Dynamic partial reconfigurable hardware architecture for principal component analysis on mobile and embedded devices. EURASIP J. Embed. Syst. 2017(1), 25 (2017)
Chen, T., et al.: Real-time MEG data-processing unit for online medical imaging and brain–computer interface: a model-based approach. Int. J. Bioelectromagn. (IJBEM) 209(1), 39–42 (2018)
Bell, A.J., Sejnowski, T.J.: An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 7(6), 1129–1159 (1995)
Stephens, M.A.: Use of the Kolmogorov–Smirnov, Cramér–Von Mises and related statistics without extensive tables. J. R. Stat. Soc. Ser. B (Methodol.) 32, 115–122 (1970)
Smirnov, N.: Table for estimating the goodness of fit of empirical distributions. Ann. Math. Stat. 19(2), 279–281 (1948)
Dammers, J., Schiek, M.: Detection of artifacts and brain responses using instantaneous phase statistics in independent components. In: Magnetoencephalography. InTech (2011)
Charoensak, C., Sattar, F.: A single-chip FPGA design for real-time ICA-based blind source separation algorithm. In: IEEE International Symposium on Circuits and Systems, 2005. ISCAS 2005. IEEE (2005)
Shyu, K.-K., et al.: Implementation of pipelined FastICA on FPGA for real-time blind source separation. IEEE Trans. Neural Netw. 19(6), 958–970 (2008)
Du, H., Qi, H., Peterson, G.D.: Parallel ICA and its hardware implementation in hyperspectral image analysis. In: Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks II, vol. 5439. International Society for Optics and Photonics (2004)
Du, H., Qi, H.: An FPGA implementation of parallel ICA for dimensionality reduction in hyperspectral images. In: 2004 IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS'04. Proceedings, vol. 5. IEEE (2004)
Huang, W.-C., et al.: FPGA implementation of 4-channel ICA for on-line EEG signal separation. In: IEEE Biomedical Circuits and Systems Conference, 2008. BioCAS 2008. IEEE (2008)
Van, L.-D., Di-You, Wu, Chen, C.-S.: Energy-efficient FastICA implementation for biomedical signal separation. IEEE Trans. Neural Netw. 22(11), 1809–1822 (2011)
Yang, C.-H., Shih, Y.-H., Chiueh, H.: An 81.6 uW FastICA processor for epileptic seizure detection. IEEE Trans. Biomed. Circuits Syst. 9(1), 60–71 (2015)
Shih, W.-Y., et al.: An effective chip implementation of a real-time eight-channel EEG signal processor based on on-line recursive ica algorithm. In: 2012 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE (2012)
Roh, T., et al.: A wearable neurofeedback system with EEG-based mental status monitoring and transcranial electrical stimulation. IEEE Trans. Biomed. Circuits Syst. 8(6), 755–764 (2014)
Kuriakose, J., George, J.K.: Multilevel Power Optimization for ICA Processor. IJSTE Int. J. Sci. Technol. Eng. 392–395 (2016)
Kim, C.-M., et al.: FPGA implementation of ICA algorithm for blind signal separation and adaptive noise canceling. IEEE Trans. Neural Netw. 14(5), 1038–1046 (2003)
Chen, T-Y.: A system-on-chip design of 32-channel EEG acquisition system with automatic artifacts rejection. MS Work. National Chiao Tung University, 2016. Web. 20 June 2018
Florin, E., Bock, E., Baillet, S.: Targeted reinforcement of neural oscillatory activity with real-time neuroimaging feedback. Neuroimage 88, 54–60 (2014)
Hesse, C.W., Oostenveld, R.: On the development of a brain–computer interface system using high-density magnetoencephalogram signals for real-time control of arobot arm. In: Proceedings of the 4th Annual Symposium of the Benelux. IEEE Engineering in Medicine and Biology Society, pp. 1–4 (2007)
Basit-Ur-Rahim, M.A., Arif, F., Ahmad, J.: Modeling of real-time embedded systems using SysML and its verification using UPPAAL and DiVinE. In: 2014 5th IEEE International Conference on Software Engineering and Service Science (ICSESS). IEEE (2014)
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The presented research is supported by China Scholarship Council (CSC), in cooperation with the Central Institute of Engineering, Electronics and Analytics - Electronic Systems (ZEA-2) and the Institute of Neuroscience and Medicine (INM-4) at Forschungszentrum Jülich GmbH.
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Communicated by Juergen Dingel.
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Chen, T., Schiek, M., Dammers, J. et al. Requirement-driven model-based development methodology applied to the design of a real-time MEG data processing unit. Softw Syst Model 19, 1567–1587 (2020). https://doi.org/10.1007/s10270-020-00797-3
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DOI: https://doi.org/10.1007/s10270-020-00797-3