• DocumentCode
    140247
  • Title

    An efficient ASIC implementation of 16-channel on-line recursive ICA processor for real-time EEG system

  • Author

    Wai-Chi Fang ; Kuan-Ju Huang ; Chia-Ching Chou ; Jui-Chung Chang ; Cauwenberghs, Gert ; Tzyy-Ping Jung

  • Author_Institution
    Dept. of Electron. Eng., Nat. Chiao-Tung Univ., Hsinchu, Taiwan
  • fYear
    2014
  • fDate
    26-30 Aug. 2014
  • Firstpage
    3849
  • Lastpage
    3852
  • Abstract
    This is a proposal for an efficient very-large-scale integration (VLSI) design, 16-channel on-line recursive independent component analysis (ORICA) processor ASIC for real-time EEG system, implemented with TSMC 40 nm CMOS technology. ORICA is appropriate to be used in real-time EEG system to separate artifacts because of its highly efficient and real-time process features. The proposed ORICA processor is composed of an ORICA processing unit and a singular value decomposition (SVD) processing unit. Compared with previous work [1], this proposed ORICA processor has enhanced effectiveness and reduced hardware complexity by utilizing a deeper pipeline architecture, shared arithmetic processing unit, and shared registers. The 16-channel random signals which contain 8-channel super-Gaussian and 8-channel sub-Gaussian components are used to analyze the dependence of the source components, and the average correlation coefficient is 0.95452 between the original source signals and extracted ORICA signals. Finally, the proposed ORICA processor ASIC is implemented with TSMC 40 nm CMOS technology, and it consumes 15.72 mW at 100 MHz operating frequency.
  • Keywords
    CMOS integrated circuits; Gaussian processes; application specific integrated circuits; bioelectric potentials; electroencephalography; independent component analysis; medical signal processing; neurophysiology; random processes; TSMC CMOS technology; average correlation coefficient; eight-channel sub-Gaussian components; eight-channel super-Gaussian components; frequency 100 MHz; power 15.72 mW; real-time EEG system; singular value decomposition processing unit; sixteen channel on-line recursive independent component analysis processor ASIC; sixteen-channel random signals; size 40 nm; very-large-scale integration design; Algorithm design and analysis; Application specific integrated circuits; Electroencephalography; Hardware; Independent component analysis; Real-time systems; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE
  • Conference_Location
    Chicago, IL
  • ISSN
    1557-170X
  • Type

    conf

  • DOI
    10.1109/EMBC.2014.6944463
  • Filename
    6944463