Real-time closed-loop tracking of an unknown number of neural sources using probability hypothesis density particle filtering

Probability hypothesis density (PHD) filtering, implemented using particle filters, is a Bayesian technique used to non-linearly track multiple objects. In this paper, we propose a new approach based on PHD particle filters (PHD-PF) to automatically track the number of magnetoencephalography (MEG) neural dipole sources and their unknown states. In particular, by separating the MEG measurements using independent component analysis, PHD-PF is applied in a closed-loop to first estimate the number of sources and then recover their amplitude, location and orientation. We also reduce the processing time and computational complexity by employing window-based processing and multi-channel decomposition. We simulate the overall system using synthetic data and show that the proposed algorithm has tracking performance similar to existing approaches with significantly fewer number of particles. We also map the algorithm on to a Xilinx Virtex-5 field-programmable gate array (FPGA) platform. The processing period for one iteration using 3,200 particles is only about 314 μs, which makes this implementation suitable for real-time tracking.

[1]  Michele Piana,et al.  Particle filtering, beamforming and multiple signal classification for the analysis of magnetoencephalography time series: A comparison of algorithms , 2010 .

[2]  J.C. Mosher,et al.  Multiple dipole modeling and localization from spatio-temporal MEG data , 1992, IEEE Transactions on Biomedical Engineering.

[3]  E. Somersalo,et al.  Non-stationary magnetoencephalography by Bayesian filtering of dipole models , 2003 .

[4]  Lauri Parkkonen,et al.  Dynamical MEG source modeling with multi‐target Bayesian filtering , 2009, Human brain mapping.

[5]  D. Chakrabarti,et al.  A fast fixed - point algorithm for independent component analysis , 1997 .

[6]  Andrzej Cichocki,et al.  Independent component analysis for unaveraged single-trial MEG data decomposition and single-dipole source localization , 2002, Neurocomputing.

[7]  D. Cohen.  Magnetoencephalography: Evidence of Magnetic Fields Produced by Alpha-Rhythm Currents , 1968, Science.

[8]  R. Mahler.  Multitarget Bayes filtering via first-order multitarget moments , 2003 .

[9]  R. Ilmoniemi,et al.  Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain , 1993 .

[10]  Sumeetpal S. Singh,et al.  Sequential monte carlo implementation of the phd filter for multi-target tracking , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[11]  D. Cohen.  Magnetoencephalography: Detection of the Brain's Electrical Activity with a Superconducting Magnetometer , 1972, Science.

[12]  Chaitali Chakrabarti,et al.  Multiple sensor sequential tracking of neural activity: Algorithm and FPGA implementation , 2010, 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers.

[13]  Richard M. Leahy,et al.  Electromagnetic brain mapping , 2001, IEEE Signal Process. Mag..

[14]  Hidehisa Shiomi,et al.  Acceleration of FPGA-based ICA processor for real-time processing , 2010, 2010 IEEE Antennas and Propagation Society International Symposium.