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http://dx.doi.org/10.4218/etrij.2019-0020

Neural source localization using particle filter with optimal proportional set resampling  

Veeramalla, Santhosh Kumar (Department of Electronics and Communication Engineering, National Institute of Technology)
Talari, V.K. Hanumantha Rao (Department of Electronics and Communication Engineering, National Institute of Technology)
Publication Information
ETRI Journal / v.42, no.6, 2020 , pp. 932-942 More about this Journal
Abstract
To recover the neural activity from Magnetoencephalography (MEG) and Electroencephalography (EEG) measurements, we need to solve the inverse problem by utilizing the relation between dipole sources and the data generated by dipolar sources. In this study, we propose a new approach based on the implementation of a particle filter (PF) that uses minimum sampling variance resampling methodology to track the neural dipole sources of cerebral activity. We use this approach for the EEG data and demonstrate that it can naturally estimate the sources more precisely than the traditional systematic resampling scheme in PFs.
Keywords
EEG; particle filter; resampling; source localization; systematic resampling;
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1 P. B. Choppala, P. D. Teal, and M. R. Frean, Soft resampling for improved information retention in particle filtering, in Proc. IEEE Int. Conf. Acoustics, Speech Signal Process (Vancouver, Canada), 2013, pp. 4036-4040.
2 T. Li, The optimal arbitrary-proportional finite-set-partitioning, Front. Inf. Technol. Electron. Eng. 16 (2015), 969-984.   DOI
3 M. Hamalainen et al., Magnetoencephalography--theory, instrumentation and applications to nonivasive studies of the working human brain, Rev. Mod. Phys. 65 (1993), 413-497.   DOI
4 C. Campi et al., A Rao-Blackwellized particle filter for magnetoencephalography, Inverse Probl. 24 (2008), 25015-25023.   DOI
5 J. Sarvas, Basic mathematical and electromagnetic concepts of the bomagnetic inverse problem, Phys. Med. Biol. 32 (1987), 11-22.   DOI
6 X. Chen, S. Sarakka, and S. Godsill, A Bayesian particle filtering method for brain source localisation, Digit. Signal Process. 47 (2015), 192-204.   DOI
7 B. Ebinger et al., EEG dynamic source localization using marginalized particle filtering, in Proc. IEEE Int. Conf. Bioinform. Biomed. (Washington, D.C., USA), Nov. 2015, pp. 454-457.
8 L. Miao et al., Algorithm and parallel implementation of particle filtering and its use in waveform-agile sensing, J. Sig. Process. Syst. 65 (2011), 211-227.   DOI
9 V. S. Kumar and T. H. Rao, Resampling schemes for RaoBlackwellization Particle Filters, In Proc. Int. Conf. Comput., Analytics Security Trends (Pune, India), Dec. 2016, pp. 377-382.
10 R. Douc and O. Cappe, Comparison of resampling schemes for particle filtering, in Proc. Int. Symp. Image Signal Process. Analysis (Zagreb, Croatia), 2005, pp. 64-69.
11 F. D. Crisan, P. Del Moral, and T. Lyons, Discrete filtering using branching and interacting particle systems, Markov Proc. Rel. Fields 5 (1999), no. 3, 293-318.
12 T. Li et al., Resampling methods for particle filtering: identical distribution, a new method and comparable study, Front. Inform. Technol. Electron. Eng. 16 (2015), no. 11, 969-984.   DOI
13 L. Miao et al., Real-time closed-loop tracking of an unknown number of neural sources using probability hypothesis density particle filtering, in Proc. IEEE Workshop Signal Process. Syst. (Beirut, Lebanon), Oct. 2011, pp. 367-372.
14 L. Miao et al., Multiple sensor sequential tracking of neural activity: Algorithm and FPGA implementation, in Proc. Conf. Record Forty Fourth Asilomar Conf. Signals, Syst. Comput (Pacific Grove, CA, USA), Nov. 2010, pp. 369-373.
15 J. D. Hol, Resampling in particle filters, Linkoping University, Dissertation, LiTH-ISY-EX-ET-0283-2004, 2004.
16 J. D. Hol, T. B. Schon, and F. Gustafsson, On resampling algorithms for particle filters, in Proc. IEEE Nonlinear Statistical Signal Process. Workshop (Cambridge, UK), 2006, pp. 79-82.
17 C. Campi et al., Highly Automated Dipole EStimation (HADES), Comput. Intell. Neurosci. 2011 (2011), 982185:1-11.
18 R. Oostenveld et al., FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data, Comput. Intell. Neurosci. 2011 (2011), 56869:1-9.
19 F. Tadel et al., Brainstorm: a user-friendly application for MEG/EEG Analysis, Comput. Intell. Neurosci. 2011 (2011), 879716:1-13.
20 M. Lascano et al., A review on non-invasive localisation of focal epileptic activity using EEG source imaging, Epileptologie 29 (2012), 80-89.
21 L. Koessler et al., Source localization of ictal epileptic activity investigated by high resolution EEG and validated by SEEG, NeuroImage 51 (2010), no. 2, 642-653.   DOI
22 P. Georgieva et al., A beamformer-particle filter framework for localization of correlated EEG Sources, IEEE J. Biomed. Health Inform. 20 (2016), no. 3, 880-892.   DOI
23 H. Stefan et al., Magnetic source localization in focal epilepsy: multichannel magnetoencephalography correlated with magnetic resonance brain imaging, Brain 113 (1990), no. 5, 1347-1359.   DOI
24 G. Ouyang et al., Global synchronization of multichannel EEG in patients with electrical status epilepticus in sleep, Clinical EEG neurosci. 46 (2015), no. 4, 357-363.   DOI
25 P. Nemtsas, Source localization of ictal epileptic activity based on high-density scalp EEG data, Epilepsia 58 (2017), 1027-1036.   DOI
26 A. Delorme and S. Makeig, EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics, J. Neurosci. Methods 134 (2004), 9-21.   DOI
27 S. Makeig et al., Dynamic brain sources of visual evoked responses, Science 295 (2002), 690-694.   DOI
28 A. Sorrentino et al., Dynamical MEG source modeling with multi-target bayesian filtering, Hum. Brain Mapp. 30 (2009), no. 6, 1911-1921.   DOI
29 M. S. Hamalainen and R. J. Ilmoniemi, Interpreting magnetic fields of the brain: minimum norm estimates, Med. Biol. Eng. Comput. 32 (1994), no. 1, 35-42.   DOI
30 K. Sekihara et al., Application of an MEG Eigenspace Beamformer to reconstructing spatio-temporal activities of neural sources, Human Brain Map 15 (2002), 199-215.   DOI
31 J. C. Mosher, P. S. Lewis, and R. M. Leahy, Multiple dipole modelling and localization from spatio-temporal MEG data, IEEE Trans. Biomed. Eng. 39 (1992), no. 6, 541-557.   DOI
32 J. M. Antelis and J. Minguez, Dynamic solution to the EEG source localization problem using Kalman filters and particle filters, in Proc. Annu. Int. Conf. IEEE Eng. Med. Bio. Soc., Minneapolis, MN, USA, Sept. 2009, pp. 77-80.
33 H. R. Mohseni, E. L. Wilding, and S. Sanei, Sequential monte carlo techniques for EEG dipole placing and tracking, in Proc. IEEE Sens. Array Multichannel Signal Process. Workshop (Darmstadt, Germany), July 2008, pp. 95-98.
34 V. Vivaldi and A. Sorrentino, Bayesian smoothing of dipoles in Magneto-/Electro-encephalography, Inverse Prob 32 (2016), 1-16.
35 H. K. Pikkarainen and J. Schicho, Bayesian Model for Root Computation, J. Math. Comput. Sci. 2 (2009), 567-586.   DOI
36 A. Doucet, N. De Freitas, and N. Gordon (eds), Sequential Monte-Carlo Methods in Practice, Springer-Verlag, New York 2001.
37 L. Miao, Efficient bayesian tracking of multiple sources of neural activity: algorithms and real-time FPGA implementation, IEEE Trans. Sig Process. 61 (2013), no. 3, 633-647.   DOI
38 P. Georgieva et al., Particle Filters and Beamforming for EEG Source Estimation, in Proc. IEEE World Congress Computat. Intell.- IJCNN (Brisbane, Australia), 2012, pp. 1100-1107.
39 X. Chen, S. Sarkka, and S. Godsill, Probabilistic initiation and termination for MEG multiple dipole localization using sequential Monte Carlo methods, in Proc. Int. Conf. Inf. Fusion (Istanbul, Turkey), July 2013, pp. 580-587.
40 M. S. Arulampalam et al., A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Trans. Signal Process. 50 (2002), no. 2, 174-188.   DOI
41 T. Li, M. Bolic, P. M. Djuric, Resampling methods for particle filtering: classification, implementation, and strategies, IEEE Signal Process. Mag. 32 (2015), 70-86.
42 M. Bolic, S. Hong, and P. M. Djuric, Resampling algorithms for particle filters a computational complexity perspective, EURASIP J. Adv. Signal Process. 15 (2004), 2267-2277.
43 S. Sarkka, A. Vehtari, and J. Lampinen, Rao-Blackwellized particle filter for multiple target tracking, Inf. Fusion 8 (2005), 2-15.   DOI
44 N. Vlassis, B. Terwijn, and B. Krose, Auxiliary particle filter robot localization from high-dimensional sensor observations, in Proc. IEEE Int. Conf. Robot. Autom. (Washington, DC, USA), 2002, pp. 7-12.
45 R. Van Der Merwe et al., The Unscented Particle Filter, Adv. Neural Inf. Process. Syst. 96 (2001), 584-590.
46 C. Musso, N. Oudjane, and F. Legland, Improving regularized particle filters, in Sequential Monte Carlo Methods in Practice, Springer, 2001, pp. 247-272.
47 F. Hutter and R. Dearden, The Gaussian particle filter for diagnosis of non-linear systems, IFAC Proc. 36 (2003), 909-914.