Estimation of Neural Sources from EEG Measurements Using Sequential Monte Carlo Method
Santhosh Kumar Veeramalla, V. K. Hanumantha Rao Talari · Ingénierie des systèmes d information · 2019
The localization of neural sources is a crucial issue in medical, scientific and technical applications.However, the dipole sources in the brain may vary in nature and change constantly with time, adding to the difficulty of source localization.In this paper, the dynamic neural sources in the brain are simulated by sequential Monte-Carlo (MC) method, based on electroencephalography (EEG) data.Firstly, the EEG data were considered as a state space model.Considering the nonlinearity of the EEG data, the sequential M-C method was introduced as a particle filter, and the Metropolis-Hastings (M-H) resampling was employed to alleviate the particle impoverishment of general particle filter.The accuracy of our method in source localization was verified through two experiments, using both synthetic and real data.The research results shed important new light on the research of brain neurology.