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Affective computing is an increasing interdisciplinary research field that provides great potential to recognize, understand and express human emotions. Recently, multimodal analysis starts to gain more popularity in affective studies, which could provide a more comprehensive view of emotion dynamics based on the diverse and complementary information from different data modalities. However, the stability and generalizability of current multimodal analysis methods have not been thoroughly developed yet. In this paper, we propose a novel multimodal analysis method (EEG-AVE : EEG with audio-visual embedding) for cross-individual affective detection, where EEG signals are exploited to identify the emotion-related individual preferences and audio-visual information is leveraged to estimate the intrinsic emotions involved in the multimedia content. EEG-AVE is composed of two main modules. For EEG-based individual preferences prediction module, a multi-scale domain adversarial neural network is developed to explore the shared dynamic, informative, and domain-invariant EEG features across individuals. For video-based intrinsic emotions estimation module, a deep audio-visual feature-based hypergraph clustering method is proposed to examine the latent relationship between semantic audio-visual features and emotions. Through an embedding model, both estimated individual preferences and intrinsic emotions are incorporated with shared weights and further contribute to affective detection across individuals. Experiments on two well-known emotional databases indicate that the proposed EEG-AVE model achieves a better performance under a leave-one-individual-out cross-validation individual-independent evaluation protocol. The results demonstrate that EEG-AVE is an effective model with good reliability and generalizability, which has practical significance in the development of multimodal analysis in affective computing. [ABSTRACT FROM AUTHOR]

The six papers included in this special issue represent a selection of extended contributions presented at the 17th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2022 held in Salamanca, Spain, September 6th-8th, 2022, and organized by the BISITE group at the University of Salamanca. The International Conference on Hybrid Artificial Intelligence Systems (HAIS 2022) has become a unique, established, and broad interdisciplinary forum for researchers and practitioners who are involved in developing and applying symbolic and sub-symbolic techniques aimed at the construction of highly robust and reliable problem-solving techniques to present the most relevant achievements in this field. HAIS Series of Conferences provides an interesting opportunity to present and discuss the latest theoretical advances and real-world applications in this multidisciplinary research field. [ABSTRACT FROM AUTHOR]

The eight papers included in this special issue represent a selection of extended contributions presented at the 16th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2021 held in Bilbao, Spain, September 22nd-24th, 2021, and organized by the BISITE group and the University of Deusto. The International Conference on Hybrid Artificial Intelligence Systems (HAIS 2021) has become a unique, established, and broad interdisciplinary forum for researchers and practitioners who are involved in developing and applying symbolic and sub-symbolic techniques aimed at the construction of highly robust and reliable problem-solving techniques to present the most relevant achievements in this field. HAIS Series of Conferences provides an interesting opportunity to present and discuss the latest theoretical advances and real-world applications in this multidisciplinary research field. [ABSTRACT FROM AUTHOR]