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EEG dynamic source localization using Marginalized Particle Filtering

机译：使用边缘化粒子滤波的脑电动态源定位

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Localization of the brain neural generators that create Electroencephalographs (EEGs) has been an important problem in clinical, research and technological applications related to the brain. The active regions in the brain are modeled as equivalent current dipoles, and the positions and moments of these dipoles or brain sources are estimated. So far, the brain dipoles are assumed to be fixed or time-invariant. However, recent neurological studies are showing that brain sources are not static but vary (in terms of location and moment) depending on various internal and external stimuli. This paper presents a shift in the current paradigm of brain source localization by considering dynamic sources in the brain. We formulate the brain source estimation problem from EEG measurements as a (nonlinear) state-space model. We use the Particle Filter (PF), essentially a sequential Monte Carlo method, to track the trajectory of the moving dipoles in the brain. We further address the “curse of dimensionality,” issue of the PF by taking advantage of the structure of the EEG state-space model, and marginalizing out the linearly evolving states. A Kalman Filter is used to optimally estimate the linear elements, whereas the PF is used to track only the non-linear components. This technique reduces the dimension of the problem; thus exponentially reducing the computational cost. Our simulation results show that, where the PF fails, the Marginalized PF is able to successfully track two dipoles in the brain with only 500 particles.
机译：产生脑电图仪（EEG）的大脑神经发生器的定位已成为与大脑相关的临床，研究和技术应用中的重要问题。将大脑中的活动区域建模为等效电流偶极子，并估算这些偶极子或脑源的位置和力矩。到目前为止，假定脑偶极子是固定的或时不变的。但是，最近的神经学研究表明，脑源不是静态的，而是根据各种内部和外部刺激而变化的（在位置和力矩方面）。通过考虑大脑中的动态源，本文提出了当前脑源定位的范式的转变。我们根据脑电图测量将脑源估计问题公式化为（非线性）状态空间模型。我们使用粒子滤波器（PF）（本质上是一种顺序蒙特卡罗方法）来跟踪大脑中移动偶极子的轨迹。我们通过利用EEG状态空间模型的结构，边缘化线性演化状态，进一步解决PF的“维数诅咒”问题。卡尔曼滤波器用于最佳估计线性元素，而PF用于仅跟踪非线性分量。这种技术减小了问题的范围。从而成倍地降低了计算成本。我们的仿真结果表明，在PF失效的情况下，边缘化PF能够仅用500个粒子成功跟踪大脑中的两个偶极子。

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