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不同复杂度心算任务基于脑电的因果连接流增益研究 被引量:1
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作者 宋利清 祖红月 王索刚 《科学技术与工程》 北大核心 2016年第15期54-59,共6页
已有的关于心算脑电的研究大多是从时域角度出发,该文旨在从频域上研究三种不同复杂度心算任务的多通道脑电的因果连结流增益特征变化,以及在不同脑区之间的差异。利用定向传递函数的方法估计得到因果连接矩阵,计算δ、θ、α、β、γ... 已有的关于心算脑电的研究大多是从时域角度出发,该文旨在从频域上研究三种不同复杂度心算任务的多通道脑电的因果连结流增益特征变化,以及在不同脑区之间的差异。利用定向传递函数的方法估计得到因果连接矩阵,计算δ、θ、α、β、γ频段下信息流增益特征,得到三种任务流增益均在中央-顶叶较活跃;并且复杂的心算相对于简单心算引起了F4通道的流增益活跃。不同脑区比较得到三种任务在高频段,尤其是β频段更具有显著性差异。复杂的心算能够增加右侧额区和高频段左颞区的信息流活动,减少顶区的信息流活动。 展开更多
关键词 心算 多通道脑电 因果连结 定向传递函数 信息流增益
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Structure and Connectivity Analysis of Financial Complex System Based on G-Causality Network
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作者 徐传明 闫妍 +2 位作者 朱晓武 李晓腾 陈晓松 《Communications in Theoretical Physics》 SCIE CAS CSCD 2013年第11期630-636,共7页
The recent financial crisis highlights the inherent weaknesses of the financial market. To explore the mechanism that maintains the financial market as a system, we study the interactions of U.S. financial market from... The recent financial crisis highlights the inherent weaknesses of the financial market. To explore the mechanism that maintains the financial market as a system, we study the interactions of U.S. financial market from the network perspective. Applied with conditional Granger causality network analysis, network density, in-degree and out-degree rankings are important indicators to analyze the conditional causal relationships among financial agents, and further to assess the stability of U.S. financial systems. It is found that the topological structure of G-causality network in U.S. financial market changed in different stages over the last decade, especially during the recent global financial crisis. Network density of the G-causality model is much higher during the period of 2007-2009 crisis stage, and it reaches the peak value in 2008, the most turbulent time in the crisis. Ranked by in-degrees and out-degrees, insurance companies are listed in the top of 68 financial institutions during the crisis. They act as the hubs which are more easily influenced by other financial institutions and simultaneously influence others during the global financial disturbance. 展开更多
关键词 conditional Granger causality network (G-causality network) network density IN-DEGREE out-degree
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