Veeramalla Santhosh Kumar, Talari V K Hanumantha Rao
Department of Electronics and Communication Engineering, National Institute of Technology, Warangal, Telangana 506004 India.
Biomed Eng Lett. 2020 Feb 6;10(2):205-215. doi: 10.1007/s13534-020-00149-6. eCollection 2020 May.
Tracking and detection of neural activity has numerous applications in the medical research field. By considering neural sources, it can be monitored by electroencephalography (EEG). In this paper, we focus primarily on developing advanced signal processing methods for locating neural sources. Due to its high performance in state estimation and tracking, particle filter was used to locate neural sources. However, particle degeneracy limits the performance of particle filters in the most utmost situations. A few resampling methods were subsequently proposed to ease this issue. These resampling methods, however, take on heavy computational costs. In this article, we aim to investigate the Partial Stratified Resampling algorithm which is time-efficient that can be used to locate neural sources and compare them to conventional resampling algorithms. This work is aimed at reflecting on the capabilities of various resampling algorithms and estimating the performance of locating neural sources. Simulated data and real EEG data are used to conduct evaluation and comparison experiments.
神经活动的追踪与检测在医学研究领域有众多应用。通过考虑神经源，可以利用脑电图（EEG）对其进行监测。在本文中，我们主要专注于开发用于定位神经源的先进信号处理方法。由于粒子滤波器在状态估计和追踪方面具有高性能，因此被用于定位神经源。然而，粒子退化在大多数极端情况下限制了粒子滤波器的性能。随后提出了一些重采样方法来缓解这个问题。然而，这些重采样方法计算成本高昂。在本文中，我们旨在研究部分分层重采样算法，该算法具有时间效率，可用于定位神经源，并将其与传统重采样算法进行比较。这项工作旨在思考各种重采样算法的能力，并评估定位神经源的性能。使用模拟数据和真实EEG数据进行评估和比较实验。
