首页> 外文会议>The 7th International Conference on Cognitive Science(第七届国际认知科学大会 ICCS 2010) >A Kalman Smoother-Based Approach for Estimating Time-Varying Cortical Connectivity from High-Density EEG
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A Kalman Smoother-Based Approach for Estimating Time-Varying Cortical Connectivity from High-Density EEG

机译:基于卡尔曼平滑器的高密度脑电估计随时间变化的皮质连通性的方法

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Inferring the cortical connectivity from highdensity electroencephalogram (EEG) is attracting growing attention in the filed of cognitive neuroscience as it provides the time-varying patterns of  information transfer among distributed cortical areas with high time resolution and satisfactory spatial resolution. Currently, one commonly-used approach to investigate the cortical connectivity is the Granger causality, which describes the multichannel high-density EEG data as a multivarjate autoregressive (MVAR) model. A set of measures of the dynamic cortical connectivity, such as the directed transfer function (DTF) and the partial directed coherence (PDC), can be obtained from the MVAR coefficient estimates. Identification of the MVAR model is conventionally achieved by the" sliding-window approach or the recursive least squares (RLS) algorithm. However, these methods often exhibit considerably large variability when dealing with high-dimensional EEG, which implies that a large number of parameters are to be estimated from a limited number of measurements. Therefore, the sliding window and the RLS methods cannot accurately estimate the dynamic MVAR coefficients, possibly leading to a wrong interpretation of cortical connectivity.
机译:从高密度脑电图(EEG)推断皮质连通性在认知神经科学领域引起了越来越多的关注,因为它提供了具有高时间分辨率和令人满意的空间分辨率的分布式皮质区域之间信息传递的时变模式。当前,研究皮层连通性的一种常用方法是Granger因果关系,该因果关系将多通道高密度EEG数据描述为多变量自回归(MVAR)模型。可以从MVAR系数估计中获得一组动态皮质连通性的量度,例如定向传递函数(DTF)和部分定向相干性(PDC)。 MVAR模型的识别通常是通过“滑动窗口方法或递归最小二乘(RLS)算法实现的。”但是,这些方法在处理高维EEG时通常表现出很大的可变性,这意味着需要使用大量参数因此,滑动窗口和RLS方法无法准确估计动态MVAR系数,可能导致对皮质连通性的错误解释。

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