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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Comparison Analysis: Granger Causality and New Causality and Their Applications to Motor Imagery
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Comparison Analysis: Granger Causality and New Causality and Their Applications to Motor Imagery

机译：比较分析：Granger因果关系和新因果关系及其在运动图像中的应用

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摘要

In this paper we first point out a fatal drawback that the widely used Granger causality (GC) needs to estimate the autoregressive model, which is equivalent to taking a series of backward recursive operations which are infeasible in many irreversible chemical reaction models. Thus, new causality (NC) proposed by Hu et al. (2011) is theoretically shown to be more sensitive to reveal true causality than GC. We then apply GC and NC to motor imagery (MI) which is an important mental process in cognitive neuroscience and psychology and has received growing attention for a long time. We study causality flow during MI using scalp electroencephalograms from nine subjects in Brain-computer interface competition IV held in 2008. We are interested in three regions: Cz (central area of the cerebral cortex), C3 (left area of the cerebral cortex), and C4 (right area of the cerebral cortex) which are considered to be optimal locations for recognizing MI states in the literature. Our results show that: 1) there is strong directional connectivity from Cz to C3/C4 during left- and right-hand MIs based on GC and NC; 2) during left-hand MI, there is directional connectivity from C4 to C3 based on GC and NC; 3) during right-hand MI, there is strong directional connectivity from C3 to C4 which is much clearly revealed by NC than by GC, i.e., NC largely improves the classification rate; and 4) NC is demonstrated to be much more sensitive to reveal causal influence between different brain regions than GC.
机译：在本文中，我们首先指出一个致命的缺点，即广泛使用的格兰杰因果关系（GC）需要估计自回归模型，这相当于采取一系列在许多不可逆化学反应模型中不可行的反向递归操作。因此，Hu等人提出了新的因果关系（NC）。 （2011）从理论上讲比GC更能揭示真实的因果关系。然后，我们将GC和NC应用于运动图像（MI），这是认知神经科学和心理学中的重要心理过程，并且在很长一段时间内受到越来越多的关注。我们在2008年举行的第四届脑机接口竞赛中使用9名受试者的头皮脑电图研究了MI期间的因果流。我们对三个区域感兴趣：Cz（大脑皮层中央区域），C3（大脑皮层左侧区域）， C4和C4（大脑皮层的右侧区域）被认为是文献中识别MI状态的最佳位置。我们的结果表明：1）在基于GC和NC的左右手MI中，从Cz到C3 / C4有很强的方向连通性； 2）在左手MI期间，基于GC和NC从C4到C3有方向性连接； 3）在右手MI期间，从C3到C4有很强的方向连通性，与NC相比，NC清楚地表明了这一点，即NC大大提高了分类率； 4）与GC相比，NC对揭示不同大脑区域之间的因果影响更敏感。

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