Tytuł artykułu
Treść / Zawartość
Pełne teksty:
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
High-density electroencephalographic (EEG) systems are utilized in the study of the human brain and its underlying behaviors. However, working with EEG data requires a well-cleaned signal, which is often achieved through the use of independent component analysis (ICA) methods. The calculation time for these types of algorithms is the longer the more data we have. This article presents a hybrid implementation of the fastICA algorithm that uses parallel programming techniques (libraries and extensions of the Intel processors and CUDA programming), which results in a significant acceleration of execution time on selected architectures.
Wydawca
Czasopismo
Rocznik
Tom
Strony
455--472
Opis fizyczny
Bibliogr. 28 poz., rys., tab., wykr.
Twórcy
autor
- Maria Curie-Sklodowska University, Department of Neuroinformatics and BiomedicalEngineering, Institute of Computer Science, ul. Akademicka 9, 20-033 Lublin, Poland
autor
- Maria Curie-Sklodowska University, Department of Neuroinformatics and BiomedicalEngineering, Institute of Computer Science, ul. Akademicka 9, 20-033 Lublin, Poland
autor
- Maria Curie-Sklodowska University, Department of Information Systems Software,Institute of Computer Science, ul. Akademicka 9, 20-033 Lublin, Poland
Bibliografia
- [1] Albajes-Eizagirre A., Dubreuil Vall L., David I.S., Riera A., Soria-Frisch A.,Dunne S., Ruffini G.:EEG/ERP analysis: methods and applications, CRC Press, Boca Raton, 2014.
- [2] Brown G.D., Yamada S., Sejnowski T.J.: Independent component analysis atthe neural cocktail party,Trends in Neurosciences, vol. 24(1), pp. 54–63, 2001.doi: 10.1016/S0166-2236(00)01683-0.
- [3] Delorme A., Sejnowski T., Makeig S.: Enhanced detection of artifacts in EEGdata using higher-order statistics and independent component analysis, Neuroimage, vol. 34(4), pp. 1443–1449, 2007. doi: 10.1016/j.neuroimage.2006.11.004.
- [4] Dickter C.L., Kieffaber P.D.:EEG methods for the psychological sciences, SAGEKnowledge, Los Angeles, 2014.
- [5] Duch W., Nowak W., Meller J., Osiński G., Dobosz K., Mikołajewski D.,Wójcik G.M.: Computional approach to understanding autism spectrumdisorders, Computer Science, vol.13(2), pp. 47–61, 2012. doi: 10.7494 /csci.2012.13.2.47.
- [6] Gajos A., Wójcik G.M.: Independent component analysis of EEG data for EGIsystem, Bio-Algorithms and Med-Systems, vol. 12(2), pp. 67–72, 2016.
- [7] Gajos-Balińska A., Wójcik G.M., Stpiczyński P.: Concept of independent component analysis algorithm parallelisation. In: M. Bubak, M. Turala, K. Wiatr(eds.), Proceedings of Cracow Grid Workshop – CGW’15, pp. 55–56, 2015.
- [8] Gajos-Balińska A., Wójcik G.M., Stpiczyński P.: Parallel independent componentanalysis algorithm – performance comparison for EEG signal. In: M. Bubak, M. Turala, K. Wiatr (eds.), CGW Workshop ’17, Kraków, Poland, October 23–25, 2017: proceedings, pp. 33–34, 2017.
- [9] Gajos-Balińska A., Wójcik G.M., Stpiczyński P.: High performance optimization of independent component analysis algorithm for EEG data, Lecture Notes in Computer Science, vol. 10777, pp. 495–504, 2018.
- [10] Gajos-Balińska A., Wójcik G.M., Stpiczyński P.: Cooperation of CUDA and Intel multi-core architecture in the independent component analysis algorithmfor EEG data, Bio-Algorithms and Med-Systems, vol. 16(3), 2020. doi: 10.1515/bams-2020-0044.
- [11] Hyvarinen A.: Fast and robust fixed-point algorithms for independent component analysis, IEEE Transactions on Neural Networks, vol. 10(3), pp. 626–634, 1999.
- [12] Hyvarinen A., Oja E.: Independent component analysis: algorithms and applications, Neural Networks, vol. 13(4), pp. 411–430, 2000. doi: 10.1016/s0893-6080(00)00026-5.
- [13] Kawala-Janik A., Bauer W., Al-Bakri A., Haddix C., Yuvaraj R., Cichon K.,Podraza W.: Implementation of low-pass fractional filtering for the purpose of analysis of electroencephalographic signals. In: Conference on Non-integer Order Calculus and Its Applications, pp. 63–73, Springer, 2017.
- [14] Lastovetsky A., Szustak L., Wyrzykowski R.: Model-based optimization of EULAG kernel on Intel Xeon Phi through load imbalancing, IEEE Transactions on Parallel and Distributed Systems, vol. 28(3), pp. 787–797, 2016.doi: 10.1109/TPDS.2016.2599527.
- [15] Miko lajewska E., Miko lajewski D.: Integrated IT environment for people with disabilities: a new concept, Open Medicine, vol. 9(1), pp. 177–182, 2014.doi: 10.2478/s11536-013-0254-6.
- [16] Mikołajewska E., Mikołajewski D.: The prospects of brain – computer interface applications in children, Open Medicine, vol. 9(1), pp. 74–79, 2014. doi: 10.2478/s11536-013-0249-3.
- [17] NetStation acquisition. Technical manual. Documentation, EGI, 2011.
- [18] Rahman R.: Intel Xeon Phi coprocessor architecture and tools: the guide for application developers, Apress, Berkely, CA, USA, 2013.
- [19] Rojek I., Macko M., Mikolajewski D., Saga M., Burczynski T.: Modern methods in the field of machine modelling and simulation as a research and practical issue related to Industry 4.0, The Bulletin of the Polish Academy of Sciences Technical Sciences, vol. 69(2), 2021. doi: 10.24425/bpasts.2021.136717.
- [20] Szustak L.: Strategy for data-flow synchronizations in stencil parallel computations on multi-/manycore systems, The Journal of Supercomputing, vol. 74(4),pp. 1534–1546, 2018.
- [21] Szustak L., Bratek P.: Performance portable parallel programming of heterogeneous stencils across shared-memory platforms with modern Intel processors,The International Journal of High Performance Computing Applications, vol. 33(3), pp. 534–553, 2019. doi: 10.1177/1094342019828153.
- [22] Tadeusiewicz R. (ed.): Neurocybernetyka teoretyczna, Wydawnictwa Uniwersytetu Warszawskiego, 2009.
- [23] Ungureanu M., Bigan C., Strungaru R., Lazarescu V.: Independent component analysis applied in biomedical signal processing, Measurement Science Review,vol. 4(2), p. 18, 2004.
- [24] Wojcik G.M., Masiak J., Kawiak A., Kwasniewicz L., Schneider P., Polak N., Gajos-Balinska A.: Mapping the Human Brain in Frequency Band Analysis of Brain Cortex Electroencephalographic Activity for Selected Psychiatric Disorders, Frontiers in Neuroinformatics, vol. 12, 2018. doi: 10.3389/fninf.2018.00073.
- [25] Wojcik G.M., Masiak J., Kawiak A., Kwasniewicz L., Schneider P., Postepski F., Gajos-Balinska A.: Analysis of Decision-Making Process Using Methods of Quan-titative Electroencephalography and Machine Learning Tools, Frontiers in Neuroinformatics, vol. 13, 2019. doi: 10.3389/fninf.2019.00073.
- [26] Wojcik G.M., Masiak J., Kawiak A., Schneider P., Kwasniewicz L., Polak N.,Gajos-Balinska A.: New Protocol for Quantitative Analysis of Brain Cortex Electroencephalographic Activity in Patients With Psychiatric Disorders, Frontiersin Neuroinformatics, vol. 12, 2018. doi: 10.3389/fninf.2018.00027.
- [27] Wójcik G.M.: Selected methods of quantitative analysis in electroencephalography. In: I. Roterman-Konieczna (ed.), Simulations in Medicine, pp. 35–54,De Gruyter, 2020. doi: 10.1515/9783110667219-003.
- [28] Wyrzykowski R., Szustak L., Rojek K.: Parallelization of 2D MPDATA EULAG algorithm on hybrid architectures with GPU accelerators, Parallel Computing, vol. 40(8), pp. 425–447, 2014. doi: 10.1016/j.parco.2014.04.009.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0db0cf8b-b577-4b38-9de0-5234f166f287
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.