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 - Asilomar Conference on Signals, Systems and Computers
Pages369-373
Number of pages5
DOIs
StatePublished - 2010
Event44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010 - Pacific Grove, CA, United States
Duration: Nov 7 2010Nov 10 2010

Other

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

Fingerprint

Magnetoencephalography
Field programmable gate arrays (FPGA)
Brain
Hardware
Sensors
Computer simulation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing

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 - Asilomar Conference on Signals, Systems and Computers (pp. 369-373). [5757537] https://doi.org/10.1109/ACSSC.2010.5757537

Multiple sensor sequential tracking of neural activity : Algorithm and FPGA implementation. / Miao, Lifeng; Zhang, Jun Jason; Chakrabarti, Chaitali; Papandreou-Suppappola, Antonia.

Conference Record - Asilomar Conference on Signals, Systems and Computers. 2010. p. 369-373 5757537.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Miao, L, Zhang, JJ, Chakrabarti, C & Papandreou-Suppappola, A 2010, Multiple sensor sequential tracking of neural activity: Algorithm and FPGA implementation. in Conference Record - Asilomar Conference on Signals, Systems and Computers., 5757537, pp. 369-373, 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010, Pacific Grove, CA, United States, 11/7/10. https://doi.org/10.1109/ACSSC.2010.5757537
Miao L, Zhang JJ, Chakrabarti C, Papandreou-Suppappola A. Multiple sensor sequential tracking of neural activity: Algorithm and FPGA implementation. In Conference Record - Asilomar Conference on Signals, Systems and Computers. 2010. p. 369-373. 5757537 https://doi.org/10.1109/ACSSC.2010.5757537
Miao, Lifeng ; Zhang, Jun Jason ; Chakrabarti, Chaitali ; Papandreou-Suppappola, Antonia. / Multiple sensor sequential tracking of neural activity : Algorithm and FPGA implementation. Conference Record - Asilomar Conference on Signals, Systems and Computers. 2010. pp. 369-373
@inproceedings{98f2cccd05244e97b53c182e628a5114,
title = "Multiple sensor sequential tracking of neural activity: Algorithm and FPGA implementation",
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.",
author = "Lifeng Miao and Zhang, {Jun Jason} and Chaitali Chakrabarti and Antonia Papandreou-Suppappola",
year = "2010",
doi = "10.1109/ACSSC.2010.5757537",
language = "English (US)",
isbn = "9781424497218",
pages = "369--373",
booktitle = "Conference Record - Asilomar Conference on Signals, Systems and Computers",

}

TY - GEN

T1 - Multiple sensor sequential tracking of neural activity

T2 - Algorithm and FPGA implementation

AU - Miao, Lifeng

AU - Zhang, Jun Jason

AU - Chakrabarti, Chaitali

AU - Papandreou-Suppappola, Antonia

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=79957993600&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79957993600&partnerID=8YFLogxK

U2 - 10.1109/ACSSC.2010.5757537

DO - 10.1109/ACSSC.2010.5757537

M3 - Conference contribution

AN - SCOPUS:79957993600

SN - 9781424497218

SP - 369

EP - 373

BT - Conference Record - Asilomar Conference on Signals, Systems and Computers

ER -