School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China.
School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China.
J Neurosci Methods. 2021 Oct 1;362:109320. doi: 10.1016/j.jneumeth.2021.109320. Epub 2021 Aug 11.
Emotions play a crucial role in human communication and affect all aspects of human life. However, to date, there have been few studies conducted on how movements under different emotions influence human brain activity and cortico-muscular coupling (CMC).
In this study, for the first time, electroencephalogram (EEG) and electromyogram physiological electrical signals were used to explore this relationship. We performed frequency domain and nonlinear dynamics analyses on EEG signals and used transfer entropy to explore the CMC associated with the emotion-movement relationship. To study the transmission of information between different brain regions, we also constructed a functional brain network and calculated various network metrics using graph theory.
We found that, compared with a neutral emotional state, movements made during happy and sad emotions had increased CMC strength and EEG power and complexity. The functional brain network metrics of these three emotional states were also different.
Much of the emotion-movement relationship research has been based on subjective expression and external performance. Our research method, however, focused on the processing of physiological electrical signals, which contain a wealth of information and can objectively reveal the inner mechanisms of the emotion-movement relationship.
Different emotional states can have a significant influence on human movement. This study presents a detailed introduction to brain activity and CMC.
情绪在人类交流中起着至关重要的作用，影响着人类生活的方方面面。然而，迄今为止，关于不同情绪下的运动如何影响人类大脑活动和皮质肌电耦合（CMC）的研究甚少。
本研究首次使用脑电图（EEG）和肌电图生理电信号来探索这种关系。我们对 EEG 信号进行了频域和非线性动力学分析，并使用传递熵来探索与情绪-运动关系相关的 CMC。为了研究不同脑区之间的信息传递，我们还构建了功能脑网络，并使用图论计算了各种网络指标。
与中性情绪状态相比，在愉快和悲伤情绪下进行运动时，CMC 强度和 EEG 功率及复杂度增加。这三种情绪状态的功能脑网络指标也不同。
大部分情绪-运动关系的研究都是基于主观表达和外部表现。然而，我们的研究方法侧重于生理电信号的处理，这些信号包含丰富的信息，可以客观地揭示情绪-运动关系的内在机制。
不同的情绪状态会对人体运动产生显著影响。本研究详细介绍了大脑活动和 CMC。
