不同情绪状态下脑网络的信息流向研究

INFORMATION FLOW UNDER DIFFERET EMOTIONAL STATES IN BRAIN NETWORK

脑功能网络的信息流向能反映不同脑区之间的因果关系,通过研究不同情绪状态下脑区间的因果关系,不仅对揭示情绪产生的机理至关重要,而且可以对情绪的产生进行有效的分析、识别和监控.在人工智能领域,有效的情绪识别将直接影响人机交互过程.本研究使用DEAP情绪数据集,基于传递熵的方法构建有向功能网络,探讨了在不同情绪状态下信息流的流向及强弱变化规律,以及不同脑区间存在的信息传递相互影响模式.研究发现,不同的情绪状态下各脑区的信息流量强度不同,唤醒度越高,信息流强度越大.信息流向和脑区各节点局部连接方式也存在显著差异.同时,进一步证明唤醒度和效价两个情绪维度上存在相互依赖性.该研究为情绪识别提供了重要的参考指标和研究思路.

The information flow of the brain functional network can reflect the causal relationship among different brain regions.Studying the causal relationship of brain regions under different emotional states is not only necessary to reveal the mechanism of emotion generation,but also to effectively analyze,identify and monitor the generation of emotion.In the field of artificial intelligence,effective emotion recognition will directly affect the human-computer interaction process.This paper used the DEAP emotional data to construct a directed network by the method of transfer entropy,discussed the flow direction and change rules of information flow under different emotional states,as well as the connection patterns existing in different brain regions.It is found that the intensity of information flow at each node is different under different emotional states.The higher the arousal,the greater the intensity of information flow.There are also significant differences in how information flows and connects.At the same time,this study further demonstrates that dependence truly exists between arousal and valence(i.e.,two dimensions of emotion),and provides an important reference direction and research idea for emotion recognition.