基于Unscented卡尔曼滤波的神经质量模型参数颅外估计
Extracranial estimation of neural mass model parameters using the Unscented Kalman Filter
摘要：
数据同化被定义为将数据与先前存在的知识相融合，特别适合于从嘈杂/不充分的观测中阐明潜在的现象。虽然这种方法已被广泛应用于不同的领域，只是最近的努力，在神经科学的问题，主要使用颅内数据，从而限制其适用性的侵入性测量涉及电极植入物。在这里，我们打算将数据同化应用于非侵入性脑电图（EEG）测量，以推断大脑状态及其特征。为此，我们使用卡尔曼滤波结合合成EEG数据与耦合的神经质量模型与阿里的头部模型，该模型将颅内信号投射到头皮上。我们的研究结果表明，使用几个颅外电极可以成功地估计神经质量及其相互作用的状态和参数，而一个单一的电极只提供了一个非常局部和不充分的系统视图。使用多个颅外电极优于仅使用一个，无论是颅内还是颅外，在各种各样的动力学行为中显示。我们的研究结果显示了该方法未来临床应用的潜力。
英文摘要：
Data assimilation, defined as the fusion of data with preexisting knowledge, is particularly suited to elucidating underlying phenomena from noisy/insufficient observations. Although this approach has been widely used in diverse fields, only recently have efforts been directed to problems in neuroscience, using mainly intracranial data and thus limiting its applicability to invasive measurements involving electrode implants. Here we intend to apply data assimilation to non-invasive electroencephalography (EEG) measurements to infer brain states and their characteristics. For this purpose, we use Kalman filtering to combine synthetic EEG data with a coupled neural-mass model together with Ary's model of the head, which projects intracranial signals onto the scalp. Our results show that using several extracranial electrodes allows to successfully estimate the state and parameters of the neural masses and their interactions, whereas one single electrode provides only a very partial and insufficient view of the system. The superiority of using multiple extracranial electrodes over using only one, be it intra- or extracranial, is shown over a wide variety of dynamical behaviours. Our results show potential towards future clinical applications of the method.
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