Abstract

We investigate the use of the particle filtering sequential Bayesian estimation technique and its hardware implementation for tracking neural activity. We propose using the multiple particle filter (MPF) approach in order to reduce the computational intensity incurred due to the large number of sensors required to observe the noninvasive magnetoencephalography (MEG) measurements needed to estimate the brain current dipole source locations and moments when tracking neural activity. The improved tracking performance of the MPF is demonstrated using numerical simulations on synthetic and real data. We also investigate the parallel implementation of the MPF algorithm on a Xilinx Virtex-5 field-programmable gate array (FPGA) platform. Our results of significant reduction in timing requirements demonstrate that the implementation is suitable for real-time tracking.

Original languageEnglish (US)
Title of host publicationConference Record of the 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010
Pages369-373
Number of pages5
DOIs
StatePublished - Dec 1 2010
Event44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010 - Pacific Grove, CA, United States
Duration: Nov 7 2010Nov 10 2010

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

Other44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010
CountryUnited States
CityPacific Grove, CA
Period11/7/1011/10/10

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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  • Cite this

    Miao, L., Zhang, J. J., Chakrabarti, C., & Papandreou-Suppappola, A. (2010). Multiple sensor sequential tracking of neural activity: Algorithm and FPGA implementation. In Conference Record of the 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010 (pp. 369-373). [5757537] (Conference Record - Asilomar Conference on Signals, Systems and Computers). https://doi.org/10.1109/ACSSC.2010.5757537