Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne, Australia.
J Neural Eng. 2012 Apr;9(2):026001. doi: 10.1088/1741-2560/9/2/026001. Epub 2012 Feb 6.
We present a model-based estimation method to reconstruct the unmeasured membrane potential of neuronal populations from a single-channel electroencephalographic (EEG) measurement. We consider a class of neural mass models that share a general structure, specifically the models by Stam et al (1999 Clin. Neurophysiol. 110 1801-13), Jansen and Rit (1995 Biol. Cybern. 73 357-66) and Wendling et al (2005 J. Clin. Neurophysiol. 22 343). Under idealized assumptions, we prove the global exponential convergence of our filter. Then, under more realistic assumptions, we investigate the robustness of our filter against model uncertainties and disturbances. Analytic proofs are provided for all results and our analyses are further illustrated via simulations.
我们提出了一种基于模型的估计方法，从单通道脑电图（EEG）测量中重建神经元群体的未测量膜电位。我们考虑了一类具有通用结构的神经质量模型，特别是 Stam 等人的模型（1999 年《临床神经生理学》110 卷 1801-13 页）、Jansen 和 Rit（1995 年《生物控制论》73 卷 357-66 页）和 Wendling 等人的模型（2005 年《临床神经生理学杂志》22 卷 343 页）。在理想化的假设下，我们证明了我们的滤波器的全局指数收敛性。然后，在更现实的假设下，我们研究了我们的滤波器对模型不确定性和干扰的鲁棒性。所有结果都提供了分析证明，我们的分析通过仿真进一步说明了。
