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


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.


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


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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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