In this study, we investigated the dynamic properties of oscillatory activities in the scalp electro-encephalographs (EEGs) of 20 participants involved in a novel dynamic manipulating task using a physical interface and a virtual feedback. The complexity of such a task a rises from the unexpected relationship between the magnitude of the motion and the feedback. The characterization of complex patterns arising from EEG is an important problem in identifying different mental intentions. We proposed a scaling analysis of phase fluctuation in the scalp EEG to discriminate the network states related to different EEG patterns, which correspond to manipulating the task with right or left movement intention. These intentions are generated while the participant is engaged in such a complex task. The phase characterization method was used to calculate the instantaneous phase from the operational EEG. Then, functional brain networks (FBNs) of 20 subjects based on the task-related EEG were constructed by phase synchronization. The degree features representing the structures and scaling components of brain networks are sensitive to the EEG patterns with left or right motor intention. The correlation between features and mental intentions was investigated by discriminant analysis. For 20 subjects, the average accuracy of state detection is [Formula: see text], and the average mean-squared error (MSE) is [Formula: see text]. The brain state depicted by the results is related to high awareness, the phase characterization is of the effectiveness in EEG processing and FBN construction and the difference of control intentions can be explored by the phase characterization method. This finding may be relevant to understanding some neuronal mechanisms underlying the attention and some applications of closed-loop control for the safety operation of tools.

中文翻译：

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动态约束对象操作中脑网络相位波动的尺度分析

在这项研究中，我们调查了 20 名参与者的头皮脑电图 (EEG) 中振荡活动的动态特性，这些参与者使用物理界面和虚拟反馈参与了一项新的动态操作任务。这种任务的复杂性源于运动幅度和反馈之间的意想不到的关系。脑电图产生的复杂模式的表征是识别不同心理意图的一个重要问题。我们提出了头皮脑电图相位波动的比例分析，以区分与不同脑电图模式相关的网络状态，这对应于以右或左运动意图操纵任务。这些意图是在参与者从事如此复杂的任务时产生的。相位表征方法用于计算来自操作脑电图的瞬时相位。然后，通过相位同步构建基于任务相关脑电图的 20 名受试者的功能性脑网络 (FBN)。代表大脑网络结构和缩放组件的度数特征对具有左右运动意图的 EEG 模式很敏感。通过判别分析研究了特征与心理意图之间的相关性。对于 20 名被试，状态检测的平均准确率为 [公式：见正文]，平均均方误差 (MSE) 为 [公式：见正文]。结果所描绘的大脑状态与高意识有关，相位表征在EEG处理和FBN构建中的有效性，并且可以通过相位表征方法来探索控制意图的差异。这一发现可能与了解注意力背后的一些神经元机制以及闭环控制在工具安全操作中的一些应用有关。