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Causality from Cz to C3/C4 or between C3 and C4 revealed by granger causality and new causality during motor imagery

机译:来自CZ至C3 / C4的因果关系或C3和C4之间的Ganger因果关系和新的因果关系在电动机图像中透露

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Interaction between different brain regions has received wide attention recently. Granger causality (GC) is one of the most popular methods to explore causality relationship between different brain regions. In 2011, Hu et. al [1] pointed out shortcomings and/or limitations of GC by using a large of number of illustrative examples and showed that GC is only a causality definition in the sense of Granger and does not reflect real causality at all, and meanwhile proposed a new causality (NC) which is shown to be more reasonable and understandable than GC by those examples. Motor imagery (MI) is an important mental process in cognitive neuroscience and cognitive psychology and has received growing attention for a long time. However, there is few work about causality flow so far during MI based on scalp EEG. In this paper, we use scalp EEG to study causality flow during MI. The scalp EEGs are from 9 subjects in BCI competition IV held in 2008 [2] and provided by Graz University of Technology. We are interested in three regions: Cz (the centre of cerebral cortex), C3 (the left of cerebral cortex) and C4 (the right of cerebral cortex) which are considered to be optimal locations for recognizing MI states in literature. We apply GC and NC to scalp EEG and find that i) there is strong directional connectivity from Cz to C3/C4 during left hand and right hand MI based on GC and NC. ii) During left hand MI, there is directional connectivity from C4 to C3 based on GC and NC. iii) During right hand MI, there is strong directional connectivity from C3 to C4 which is much clearly revealed by NC method than by GC method. iv) Our results suggest that NC method in time and frequency domains is demonstrated to be much better to reveal causal influence between different brain regions than GC method. Thus, we deeply believe that NC method will shed new light on causality analysis in economics and neuroscience.
机译:最近不同脑区之间的相互作用得到了广泛的关注。格兰杰因果关系(GC)是探讨不同脑区之间因果关系的最流行方法之一。 2011年,胡等。 al [1]通过使用大量的说明性示例指出了GC的缺点和/或局限性,并显示GC只是格兰杰感的因果关系,并且根本不会反映真正的因果关系,而且同时提出了一个新的因果关系因这些例子而言显示的因果关系(NC)比GC更合理和理解。电机图像(MI)是认知神经科学和认知心理学中的一个重要心理过程,并且长期受到关注。然而,基于头皮EEG的MI期间,迄今为止,迄今为止的因果关系很少。在本文中,我们使用Scalp EEG在MI期间研究因果关系。头皮EEG在2008年的BCI比赛IV中的9个科目来自于2008年[2],由格拉茨技术大学提供。我们对三个地区感兴趣:CZ(脑皮层的中心),C3(脑皮层的左侧)和C4(脑皮层的右侧)被认为是在文献中识别MI状态的最佳位置。我们将GC和NC应用于Scalp EEG,并找到I)基于GC和NC的左手和右手MI,CZ到C3 / C4有强烈的方向连接。 ii)在左手MI期间,基于GC和NC存在来自C4至C3的方向连接。 III)在右手Mi期间,从C3到C4有强烈的方向连接,这比NC方法明显明显多于GC方法。 iv)我们的结果表明,在时间和频域中的NC方法可以更好地揭示不同脑区之间的因果影响而不是GC方法。因此,我们非常认为,NC方法将在经济学和神经科学的因果区分析上进行新的光。

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