Liu Wei, Zheng Wei-Long, Li Ziyi, Wu Si-Yuan, Gan Lu, Lu Bao-Liang
Center for Brain-Like Computing and Machine Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Rd., Shanghai 200240, People's Republic of China.
The Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University, 800 Dongchuan Rd., Shanghai 200240, People's Republic of China.
J Neural Eng. 2022 Mar 28;19(2). doi: 10.1088/1741-2552/ac5c8d.
Cultures have essential influences on emotions. However, most studies on cultural influences on emotions are in the areas of psychology and neuroscience, while the existing affective models are mostly built with data from the same culture. In this paper, we identify the similarities and differences among Chinese, German, and French individuals in emotion recognition with electroencephalogram (EEG) and eye movements from an affective computing perspective.Three experimental settings were designed: intraculture subject dependent, intraculture subject independent, and cross-culture subject independent. EEG and eye movements are acquired simultaneously from Chinese, German, and French subjects while watching positive, neutral, and negative movie clips. The affective models for Chinese, German, and French subjects are constructed by using machine learning algorithms. A systematic analysis is performed from four aspects: affective model performance, neural patterns, complementary information from different modalities, and cross-cultural emotion recognition.From emotion recognition accuracies, we find that EEG and eye movements can adapt to Chinese, German, and French cultural diversities and that a cultural in-group advantage phenomenon does exist in emotion recognition with EEG. From the topomaps of EEG, we find that theandbands exhibit decreasing activities for Chinese, while for German and French,andbands exhibit increasing activities. From confusion matrices and attentional weights, we find that EEG and eye movements have complementary characteristics. From a cross-cultural emotion recognition perspective, we observe that German and French people share more similarities in topographical patterns and attentional weight distributions than Chinese people while the data from Chinese are a good fit for test data but not suitable for training data for the other two cultures.Our experimental results provide concrete evidence of the in-group advantage phenomenon, cultural influences on emotion recognition, and different neural patterns among Chinese, German, and French individuals.
文化对情感有着至关重要的影响。然而,大多数关于文化对情感影响的研究都集中在心理学和神经科学领域,而现有的情感模型大多是基于同一文化的数据构建的。在本文中,我们从情感计算的角度,通过脑电图(EEG)和眼动来识别中国人、德国人及法国人在情感识别方面的异同。我们设计了三种实验设置:文化内个体依赖、文化内个体独立和跨文化个体独立。在中国人、德国人及法国人观看正性、中性和负性电影片段时,同时采集他们的脑电图和眼动数据。利用机器学习算法构建中国人、德国人及法国人的情感模型。从情感模型性能、神经模式、不同模态的互补信息以及跨文化情感识别四个方面进行系统分析。从情感识别准确率来看,我们发现脑电图和眼动能够适应中国、德国和法国的文化差异,并且在基于脑电图的情感识别中确实存在文化内群体优势现象。从脑电图的拓扑图来看,我们发现中国人的α和β波段活动呈下降趋势,而德国人和法国人则相反,α和β波段活动呈上升趋势。从混淆矩阵和注意力权重来看,我们发现脑电图和眼动具有互补特征。从跨文化情感识别的角度来看,我们观察到德国人和法国人在拓扑模式和注意力权重分布上比中国人有更多相似之处,而中国人的数据与测试数据拟合良好,但不适用于其他两种文化的训练数据。我们的实验结果为群体优势现象、文化对情感识别的影响以及中国人、德国人及法国人之间不同的神经模式提供了具体证据。