SEED-VII: A Multimodal Dataset of Six Basic Emotions With Continuous Labels for Emotion Recognition

• Published in: IEEE transactions on affective computing Vol. 16; no. 2; pp. 969 - 985
• Main Authors: Jiang, Wei-Bang, Liu, Xuan-Hao, Zheng, Wei-Long, Lu, Bao-Liang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.04.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Affective computing; Basic emotions; Brain modeling; Computational modeling; continuous label; Datasets; EEG; Electrocardiography; Electroencephalography; Emotion recognition; Emotions; Eye movements; Labels; multimodal dataset; Physiology; Recording; Transformers; Videos
• ISSN: 1949-3045; 1949-3045
• DOI: 10.1109/TAFFC.2024.3485057

Recognizing emotions from physiological signals is a topic that has garnered widespread interest, and research continues to develop novel techniques for perceiving emotions. However, the emergence of deep learning has highlighted the need for comprehensive and high-quality emotional datasets that enable the accurate decoding of human emotions. To systematically explore human emotions, we develop a multimodal dataset consisting of six basic (happiness, sadness, fear, disgust, surprise, and anger) emotions and the neutral emotion, named SEED-VII. This multimodal dataset includes electroencephalography (EEG) and eye movement signals. The seven emotions in SEED-VII are elicited by 80 different videos and fully investigated with continuous labels that indicate the intensity levels of the corresponding emotions. Additionally, we propose a novel Multimodal Adaptive Emotion Transformer (MAET), that can flexibly process both unimodal and multimodal inputs. Adversarial training is utilized in the MAET to mitigate subject discrepancies, which enhances domain generalization. Our extensive experiments, encompassing both subject-dependent and cross-subject conditions, demonstrate the superior performance of the MAET in terms of handling various inputs. Continuous labels are used to filter the data with high emotional intensity, and this strategy is proven to be effective for attaining improved emotion recognition performance. Furthermore, complementary properties between the EEG signals and eye movements and stable neural patterns of the seven emotions are observed.