Abstract

We propose new Bayesian algorithms to automatically track current dipole sources of neural activity in real time. We integrate multiple particle filters to track the dynamic parameters of a known number of dipole sources, resulting in reducing the computational intensity incurred due to the large number of sensors required to observe magnetoencephalography (MEG) or electroencephalography (EEG) measurements. When we also need to estimate the time-varying number of dipole sources, we develop an algorithm based on applying probability hypothesis density filtering (PHDF) for multiple object tracking. The PHDF is implemented using particle filters (PF-PHDF), and it is applied in a closed-loop with MEG/EEG measurements to first estimate the number of sources and then their corresponding amplitude, location and orientation. The PF-PHDF tracking algorithm uses an online, window-based multiple channel decomposition processing approach that reduces the overall processing time and computational complexity. We demonstrate the improved performances of the proposed algorithms by simulating neural activity tracking systems with both synthetic and real data. We map the proposed algorithms onto Xilinx Virtex-5 field-programmable gate array (FPGA) platforms and demonstrate real-time tracking performance. For example, our results showed that the PF-PHDF algorithm can process 100 data samples from three dipoles in only 5.1 ms, when 3 dipole sources are present.

Original languageEnglish (US)
Article number6338353
Pages (from-to)633-647
Number of pages15
JournalIEEE Transactions on Signal Processing
Volume61
Issue number3
DOIs
StatePublished - Feb 1 2013

Keywords

  • Dipole sources
  • FPGA implementation
  • Multiple particle filters
  • Neural activity
  • Parallel architecture
  • Particle filter
  • Probability hypothesis density filter

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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