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Cluster Computing

, Volume 16, Issue 1, pp 39–53 | Cite as

Towards energy-efficient parallel analysis of neural signals

  • Dan ChenEmail author
  • Dongcuan Lu
  • Mingwei Tian
  • Shan He
  • Shuaiting Wang
  • Jian Tian
  • Chang Cai
  • Xiaoli LiEmail author
Article

Abstract

Recent advances of experimental methods and neuroscience research have made neural signals constantly massive and analysis of these signals highly compute-intensive. This study explore the possibility proposes a massively parallel approach for analysis of neural signals using General-purpose computing on the graphics processing unit (GPGPU). We demonstrate the uses and correctness of the proposed approach via a case of analyzing EEG with focal epilepsy. An experimental examination has been carried out to investigate (1) the GPGPU-aided approach’s performance and (2) energy costs of the GPGPU-aided application versus the original CPU-only systems. Experimental results indicate that the proposed approach excels in both aspects.

Keywords

Green computing General-purpose computing on the graphics processing unit Neural signals EEG 

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References

  1. 1.
    Chen, D., Theodoropoulos, G.K., Turner, S.J., Cai, W., Minson, R., Zhang, Y.: Large scale agent-based simulation on the grid. Future Gener. Comput. Syst. 24(7), 658–671 (2008) CrossRefGoogle Scholar
  2. 2.
    Chen, D., Turner, S.J., Cai, W., Theodoropoulos, G.K., Xiong, M., Lees, M.: Synchronization in federation community networks. J. Parallel Distrib. Comput. 70(2), 144–159 (2010) zbMATHCrossRefGoogle Scholar
  3. 3.
    Chen, D., Li, D., Xiong, M., Bao, H., Li, X.: GPGPU-aided ensemble empirical mode decomposition for EEG analysis during anaesthesia. IEEE Trans. Inf. Technol. Biomed. 14(6), 1417–1427 (2010) CrossRefGoogle Scholar
  4. 4.
    Finnerty, G.T., Jefferys, J.G.: 9–16 Hz oscillation precedes secondary generalization of seizures in the rat tetanus toxin model of epilepsy. J. Neurophysiol. 83, 2217–2226 (2000) Google Scholar
  5. 5.
    Finnerty, G.T., Jefferys, J.G.: Investigation of the neuronal aggregate generating seizures in the rat tetanus toxin model of epilepsy. J. Neurophysiol. 88, 2919–2927 (2002) CrossRefGoogle Scholar
  6. 6.
    Hasson, U., Skipper, Ji, Wilde, Mj., Nusbaum, Hc., Small, Sl.: Improving the analysis, storage and sharing of neuroimaging data using relational databases and distributed computing. NeuroImage 39(2), 693–706 (2008) CrossRefGoogle Scholar
  7. 7.
    Heler, T., Steen, P.A.: Assessment of anaesthesia depth. Acta Anaesthesiol. Scand. 40, 1087–1100 (1996) CrossRefGoogle Scholar
  8. 8.
    Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. 454, 903–995 (1998) MathSciNetzbMATHCrossRefGoogle Scholar
  9. 9.
    Teeters, J.L., Harris, K.D., Millman, K.J., Olshausen, B.A., Sommer, F.T.: Data sharing for computational neuroscience. Neuroinformatics 6(1), 47–55 (2008) CrossRefGoogle Scholar
  10. 10.
    Li, X.L., Li, D., Liang, Z.H., Voss, L.J., Sleigh, J.W.: Analysis of depth of anaesthesia with Hilbert-Huang spectral entropy. Clin. Neurophysiol. 119(11), 2465–2475 (2008) CrossRefGoogle Scholar
  11. 11.
    Müller, A., Osterhage, H., Sowaa, R., Andrzejakc, R.G., Mormanna, F., Lehnertz, K.: A distributed computing system for multivariate time series analyses of multichannel neurophysiological data. J. Neurosci. Methods 152(1–2), 190–201 (2006) CrossRefGoogle Scholar
  12. 12.
  13. 13.
    Owens, J.D., Houston, M., Luebke, D., Green, S., Stone, J.E., Phillips, J.C.: GPU computing. Proc. IEEE 96(5), 879–899 (2008) CrossRefGoogle Scholar
  14. 14.
    Rampil, I.J., Matteo, R.S.: Changes in EEG spectral edge frequency correlate with the hemodynamic response to laryngoscopy and intubation. Anesthesiology 67, 139–142 (1987) CrossRefGoogle Scholar
  15. 15.
    Rofouei, M., Stathopoulos, T., Ryffel, S., Kaiser, W., Sarrafzadeh, M.: Energy-aware high performance computing with graphic processing units. In: Proceedings of the 2008 Conference on Power Aware Computing and Systems (HotPower’08), p. 11. USENIX, Berkeley (2008) Google Scholar
  16. 16.
    Rossant, C., Goodman, D.F.M., Fontaine, B., Platkiewicz, J., Magnusson, A.K., Brette, R.: Fitting neuron models to spike trains. Frontiers Neurosci. 5, 9 (2011). doi: 10.3389/fnins.2011.00009 Google Scholar
  17. 17.
    Sanei, S., Chambers, J.: EEG Signal Processing, pp. 35–50. Wiley, New York (2007) Google Scholar
  18. 18.
    Schenk, O., Christena, M., Burkharta, H.: Algorithmic performance studies on graphics processing units. J. Parallel Distrib. Comput. 68(10), 1360–1369 (2008) CrossRefGoogle Scholar
  19. 19.
    Tolke, J., Krafczyk, M.: TeraFLOP computing on a desktop pc with GPUs for 3D CFD. Int. J. Comput. Fluid Dyn. 22, 443–456 (2008) CrossRefGoogle Scholar
  20. 20.
    Wang, L., von Laszewski, G., Kunze, M., Tao, J., Dayal, J.: Provide virtual distributed environments for grid computing on demand. Adv. Eng. Softw. 41(2), 213–219 (2010) zbMATHCrossRefGoogle Scholar
  21. 21.
    Wang, L., Jie, W.: Towards supporting multiple virtual private computing environments on computational Grids. Adv. Eng. Softw. 40(4), 239–245 (2009) zbMATHCrossRefGoogle Scholar
  22. 22.
    Wang, L., von Laszewski, G., Chen, D., Tao, J., Kunze, M.: Provide virtual machine information for grid computing. IEEE Trans. SMC (TSMC) 40(6), 1362–1374 (2010) Google Scholar
  23. 23.
    Wang, L., von Laszewski, G., Tao, J., Kunze, M.: Grid virtualization engine: design, implementation and evaluation. IEEE Syst. J. (ISJ) 3(4), 477–488 (2009) CrossRefGoogle Scholar
  24. 24.
    Wang, L., von Laszewski, G., Kunze, M., Tao, J.: Cloud computing: a perspective study. New Gener. Comput. 28(2), 137–146 (2010) zbMATHCrossRefGoogle Scholar
  25. 25.
    Wang, L., Fu, C.: Research advances in modern cyberinfrastructure. New Gener. Comput. 28(2), 111–112 (2010) MathSciNetCrossRefGoogle Scholar
  26. 26.
    Wilson, J.A., Williams, J.C.: Massively parallel signal processing using the graphics processing unit for real-time brain-computer interface feature extraction. Front NeuroEng. 2, 11 (2009) CrossRefGoogle Scholar
  27. 27.
    Wu, Z.H., Huang, N.E.: Ensemble empirical mode decomposition: a noise assisted data analysis method. Adv. Adap. Data Anal. 1, 1–41 (2009) CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Dan Chen
    • 1
    • 2
    Email author
  • Dongcuan Lu
    • 3
  • Mingwei Tian
    • 3
  • Shan He
    • 2
  • Shuaiting Wang
    • 3
  • Jian Tian
    • 4
  • Chang Cai
    • 1
  • Xiaoli Li
    • 3
    Email author
  1. 1.School of Computer ScienceChina University of GeosciencesWuhanChina
  2. 2.School of Computer ScienceUniversity of BirminghamBirminghamUK
  3. 3.Institute of Electrical EngineeringYanshan UniversityQinhuangdaoChina
  4. 4.School of EconomicsHuazhong University of Science & TechnologyWuhanChina

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