Unsupervised learning of brain state dynamics during emotion imagination using high-density EEG
Unsupervised learning of brain state dynamics during emotion imagination using high-density EEG
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DOI：
10.1016/j.neuroimage.2022.118873
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发表时间：
2022-01-21
期刊：
影响因子：
5.7
通讯作者：
Makeig, Scott
中科院分区：
文献类型：
--
作者：
Hsu, Sheng-Hsiou;Lin, Yayu;Makeig, Scott
该研究应用自适应混合独立分量分析(AMICA)来学习一组ICA模型，每个模型通过为每个识别的分量过程拟合分布模型来优化，同时最大化多通道EEG数据集的某些时间点内的分量过程独立性。在这里，我们对记录的长持续时间(1-2小时)、高密度(128个通道)的脑电数据应用20模型AMICA分解，同时参与者使用引导想象来想象刺激15种特定情绪体验的情景。这些分解倾向于返回识别单一情绪想象期内的时空脑电模式或状态的模型。模型概率转换反映了情绪想象过程中脑电动力学的时间进程，这种时间进程因情绪而异。解释想象中的“悲伤”和“快乐”的模型之间的转变更加突然，与参与者的报告更一致，而想象中的“满足”的转变延伸到相邻的“放松”时期。大脑可定位独立成分过程(IC)的空间分布在参与者中(跨情绪)比情绪(跨参与者)更相似。在受试者中，在左侧吻侧前额叶、后扣带回皮质、右侧脑岛、双侧感觉运动、前运动前运动和联合视觉皮质中或附近发现了情绪想象和放松之间IC空间分布(即偶极密度)不同的脑区。在积极情绪和消极情绪之间，偶极子密度没有差异。高密度脑电动力学变化的AMICA模型可能允许对情绪体验过程中的大脑动力学进行数据驱动的洞察，可能使基于脑电的情绪解码的性能得到改善，并促进我们对情绪的理解。
This study applies adaptive mixture independent component analysis (AMICA) to learn a set of ICA models, each optimized by fitting a distributional model for each identified component process while maximizing component process independence within some subsets of time points of a multi-channel EEG dataset. Here, we applied 20-model AMICA decomposition to long-duration (1-2 h), high-density (128-channel) EEG data recorded while participants used guided imagination to imagine situations stimulating the experience of 15 specified emotions. These decompositions tended to return models identifying spatiotemporal EEG patterns or states within single emotion imagination periods. Model probability transitions reflected time-courses of EEG dynamics during emotion imagination, which varied across emotions. Transitions between models accounting for imagined "grief " and "happiness " were more abrupt and better aligned with participant reports, while transitions for imagined "contentment " extended into adjoining "relaxation " periods. The spatial distributions of brain-localizable independent component processes (ICs) were more similar within participants (across emotions) than emotions (across participants). Across participants, brain regions with differences in IC spatial distributions (i.e., dipole density) between emotion imagination versus relaxation were identified in or near the left rostrolateral prefrontal, posterior cingulate cortex, right insula, bilateral sensorimotor, premotor, and associative visual cortex. No difference in dipole density was found between positive versus negative emotions. AMICA models of changes in high-density EEG dynamics may allow data-driven insights into brain dynamics during emotional experience, possibly enabling the improved performance of EEG-based emotion decoding and advancing our understanding of emotion.