A Bayesian particle filtering method for brain source localisation

@article{Chen2015ABP,
  title={A Bayesian particle filtering method for brain source localisation},
  author={X. Chen and Simo S{\"a}rkk{\"a} and Simon J. Godsill},
  journal={Digit. Signal Process.},
  year={2015},
  volume={47},
  pages={192-204}
}
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9 Citations
Background Citations
2
Methods Citations
3

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9 Citations

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References

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TLDR
A multi-target detection and particle filtering algorithm is developed to estimate the dipolar source dynamics, and a minimum norm (MN) based estimation method is incorporated to construct the birth-death move for the dynamical number of dipolar sources.
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Efficient Bayesian Tracking of Multiple Sources of Neural Activity: Algorithms and Real-Time FPGA Implementation
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New Bayesian algorithms to automatically track current dipole sources of neural activity in real time and an online, window-based multiple channel decomposition processing approach that reduces the overall processing time and computational complexity are proposed.
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Assessment of model fit via marginal likelihood showed a clear preference for the proposed model and the associated reconstructions show better localisation.
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