Vice Chancellery for Research and Technology, University of Tabriz Advanced Signal Processing 2676-3397 5 1 2021 08 23 Combination of Sequential Particle Filter and Beamformer for the Localization of Brain Disruptive Sources Combination of Sequential Particle Filter and Beamformer for the Localization of Brain Disruptive Sources 89 104 13930 10.22034/jasp.2021.45667.1141 FA Seyed Morteza Nourian Najafabadi Electrical Engineering Department, Yazd University, Yazd, Iran Hamid Reza Abutalebi Electrical Engineering Department, Yazd University, Yazd, Iran Vahid Abootalebi Electrical Engineering Department, Yazd University, Yazd, Iran Farzaneh Shayegh Electrical and Computer Engineering Department, Isfahan University of Technology, Isfahan, Iran Journal Article 2021 04 28 This paper deals with locating disruptive sources in patients with brain disorders, supposing to have the location of active brain sources in healthy people according to their functional connectivity pattern information in similar activities. In the proposed algorithm, firstly the effect of sources that are active in normal brain activity is eliminated from the patient’s recorded EEG signals using the LCMV beamformer. Then, the disruptive sources are localized. The proposed method utilizes a combination of Sequential Particle Filter (SPF) and LCMV Beam-Former (BF) to localize disruptive sources. The simulations have been performed using BrainStorm software and pseudo-real EEG signals. The results of applying the proposed method (SPF-BF) on the simulated EEG signal show that this method could achieve better results in severe noise conditions than the LCMV beamformer, traditional particle filter algorithms, and combination of them. Also, the comparative results of the proposed method and sLORETA confirm the proper performance of the proposed method. In addition, the proposed method outperforms the other methods in terms of computational complexity. This paper deals with locating disruptive sources in patients with brain disorders, supposing to have the location of active brain sources in healthy people according to their functional connectivity pattern information in similar activities. In the proposed algorithm, firstly the effect of sources that are active in normal brain activity is eliminated from the patient’s recorded EEG signals using the LCMV beamformer. Then, the disruptive sources are localized. The proposed method utilizes a combination of Sequential Particle Filter (SPF) and LCMV Beam-Former (BF) to localize disruptive sources. The simulations have been performed using BrainStorm software and pseudo-real EEG signals. The results of applying the proposed method (SPF-BF) on the simulated EEG signal show that this method could achieve better results in severe noise conditions than the LCMV beamformer, traditional particle filter algorithms, and combination of them. Also, the comparative results of the proposed method and sLORETA confirm the proper performance of the proposed method. In addition, the proposed method outperforms the other methods in terms of computational complexity. https://jasp.tabrizu.ac.ir/article_13930_63f6ce4a354923382690a8f93941ec27.pdf