Rajabioun Mehdi, Nasrabadi Ali Motie, Shamsollahi Mohammad Bagher
Science and Research Branch, Islamic Azad University, Tehran, Iran.
Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran.
Australas Phys Eng Sci Med. 2017 Sep;40(3):675-686. doi: 10.1007/s13246-017-0578-7. Epub 2017 Aug 29.
Effective connectivity is one of the most important considerations in brain functional mapping via EEG. It demonstrates the effects of a particular active brain region on others. In this paper, a new method is proposed which is based on dual Kalman filter. In this method, firstly by using a brain active localization method (standardized low resolution brain electromagnetic tomography) and applying it to EEG signal, active regions are extracted, and appropriate time model (multivariate autoregressive model) is fitted to extracted brain active sources for evaluating the activity and time dependence between sources. Then, dual Kalman filter is used to estimate model parameters or effective connectivity between active regions. The advantage of this method is the estimation of different brain parts activity simultaneously with the calculation of effective connectivity between active regions. By combining dual Kalman filter with brain source localization methods, in addition to the connectivity estimation between parts, source activity is updated during the time. The proposed method performance has been evaluated firstly by applying it to simulated EEG signals with interacting connectivity simulation between active parts. Noisy simulated signals with different signal to noise ratios are used for evaluating method sensitivity to noise and comparing proposed method performance with other methods. Then the method is applied to real signals and the estimation error during a sweeping window is calculated. By comparing proposed method results in different simulation (simulated and real signals), proposed method gives acceptable results with least mean square error in noisy or real conditions.
有效连接性是通过脑电图进行脑功能映射时最重要的考虑因素之一。它展示了特定活跃脑区对其他脑区的影响。本文提出了一种基于双卡尔曼滤波器的新方法。在该方法中，首先通过使用一种脑活动定位方法（标准化低分辨率脑电磁断层扫描）并将其应用于脑电图信号，提取活跃区域，然后将适当的时间模型（多元自回归模型）拟合到提取的脑活动源，以评估源之间的活动和时间依赖性。接着，使用双卡尔曼滤波器来估计模型参数或活跃区域之间的有效连接性。该方法的优点是在计算活跃区域之间有效连接性的同时，能够同时估计不同脑区的活动。通过将双卡尔曼滤波器与脑源定位方法相结合，除了估计各部分之间的连接性外，还能在时间过程中更新源活动。首先通过将该方法应用于具有活跃部分之间相互作用连接性模拟的模拟脑电图信号来评估所提方法的性能。使用具有不同信噪比的有噪声模拟信号来评估方法对噪声的敏感性，并将所提方法的性能与其他方法进行比较。然后将该方法应用于真实信号，并计算扫描窗口期间的估计误差。通过比较所提方法在不同模拟（模拟信号和真实信号）中的结果，所提方法在有噪声或真实条件下以最小均方误差给出了可接受的结果。
