Abstract

We investigate and demonstrate the sparsity of electroencephalography (EEG) signals in the spatial domain by incorporating grid spacing in the area of the head enclosing the brain volume. We exploit this spatial sparsity and propose a new approach for tracking neural activity that is based on compressive particle filtering. Our approach results in reducing the number of EEG channels required to be stored and processed for neural tracking using particle filtering. Simulations using both synthetic and real EEG signals illustrate that the proposed algorithm has tracking performance comparable to existing methods while using only a reduced set of EEG channels.

Original languageEnglish (US)
Title of host publication2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages3461-3464
Number of pages4
DOIs
StatePublished - Oct 23 2012
Event2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
Duration: Mar 25 2012Mar 30 2012

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
CountryJapan
CityKyoto
Period3/25/123/30/12

Keywords

  • Compressive sensing
  • EEG
  • dipole model
  • multiple particle filter

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Miao, L., Zhang, J. J., Papandreou-Suppappola, A., & Chakrabarti, C. (2012). Neural activity tracking using spatial compressive particle filtering. In 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings (pp. 3461-3464). [6288661] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2012.6288661