基于通用赤池信息量准则改进维纳-格兰杰因果索引算法的颅内脑电效应连通性研究

Research on effective connectivity of intracerebral electroencephalogram based on Wiener-Granger Causality Index modified by generalized Akaike's Information Criterion

本文的目标是处理并分析使用深度电极在难治性癫痫患者癫痫发作期间其大脑皮层中记录到的癫痫脑电信号间的大脑效应连通性。维纳-格兰杰因果索引算法是一种众所周知的检测脑电信号间大脑效应连通性的有效方法。它是一种基于线性自回归模型的方法,而模型参数估计问题在其用于脑电因果效应连通性研究中的计算准确性与鲁棒性方面起着至关重要的作用。本文针对这一问题,使用了我们提出的改进的赤池信息量准则来估计算法中自回归模型的模型阶数,以提高维纳-格兰杰因果索引算法检测大脑效应连通性的性能。实验仿真结果表明:不管是在线性随机系统中还是在能生成模拟癫痫信号的生理模型中,该改进的维纳-格兰杰因果索引算法在检测脑效应连通性上都表现出良好的鲁棒性。

The objective is to deal with brain effective connectivity among epilepsy electroencephalogram（EEG）signals recorded by use of depth electrodes in the cerebral cortex of patients suffering from refractory epilepsy during their epileptic seizures.The Wiener-Granger Causality Index（WGCI） is a well-known effective measure that can be useful to detect causal relations of interdependence in these kinds of EEG signals.It is based on the linear autoregressive model,and the issue of the estimation of the model parameters plays an important role in the calculation accuracy and robustness of WGCI to do research on brain effective connectivity.Focusing on this issue,a modified Akaike＇s information criterion algorithm is introduced in the computation of the WGCI to estimate the orders involved in the underlying models and in order to advance the performance of WGCI to detect brain effective connectivity.Experimental results support the interesting performance of the proposed algorithm to characterize the information flow both in a linear stochastic system and a physiology-based model.