Recently, the representation of emotions in the Valence, Arousal and Dominance (VAD) space has drawn enough attention. However, the complex nature of emotions and the subjective biases in self-reported values of VAD make the emotion model too specific to a particular experiment. This study aims to develop a generic model representing emotions using a fuzzy VAD space and improve emotion recognition by utilizing this representation. We partitioned the crisp VAD space into a fuzzy VAD space using low, medium and high type-2 fuzzy dimensions to represent emotions. A framework that integrates fuzzy VAD space with EEG data has been developed to recognize emotions. The EEG features were extracted using spatial and temporal feature vectors from time-frequency spectrograms, while the subject-reported values of VAD were also considered. The study was conducted on the DENS dataset, which includes a wide range of twenty-four emotions, along with EEG data and subjective ratings. The study was validated using various deep fuzzy framework models based on type-2 fuzzy representation, cuboid probabilistic lattice representation and unsupervised fuzzy emotion clusters. These models resulted in emotion recognition accuracy of 96.09\%, 95.75\% and 95.31\%, respectively, for the classes of 24 emotions. The study also included an ablation study, one with crisp VAD space and the other without VAD space. The result with crisp VAD space performed better, while the deep fuzzy framework outperformed both models. The model was extended to predict cross-subject cases of emotions, and the results with 78.37\% accuracy are promising, proving the generality of our model. The generic nature of the developed model, along with its successful cross-subject predictions, gives direction for real-world applications in the areas such as affective computing, human-computer interaction, and mental health monitoring.

翻译：近年来，情绪在效价- arousal - 优势度（VAD）空间中的表征已引起广泛关注。然而，情绪的复杂本质以及VAD自评值存在的主观偏差使得情绪模型过度依赖特定实验情境。本研究旨在通过构建基于模糊VAD空间的通用情绪表征模型，并利用该表征提升情绪识别性能。我们采用低、中、高三种类型-2模糊维度将清晰VAD空间划分为模糊VAD空间进行情绪表征，开发了融合模糊VAD空间与脑电数据的情绪识别框架。通过时频谱图提取时空特征向量获取脑电特征，同时纳入受试者自评VAD值。基于包含24种广泛情绪类型及脑电数据与主观评分的DENS数据集，本研究采用三种深度模糊框架模型进行验证：类型-2模糊表征模型、长方体概率晶格表征模型及无监督模糊情绪聚类模型。在24类情绪识别任务中，三者分别达到96.09%、95.75%和95.31%的准确率。消融实验表明，采用清晰VAD空间的模型性能更优，而深度模糊框架模型较两者均有显著提升。将该模型扩展至跨被试情绪预测时，78.37%的准确率验证了模型泛化能力。本模型兼具通用性与跨被试预测能力，为情感计算、人机交互及心理健康监测等实际应用领域提供了发展方向。