Enhancing cross-subject emotion recognition precision through unimodal EEG: a novel emotion preceptor model

• Published in: Brain informatics Vol. 11; no. 1; pp. 31 - 9
• Main Authors: Dong, Yihang, Jing, Changhong, Mahmud, Mufti, Ng, Michael Kwok-Po, Wang, Shuqiang
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024; Springer; Springer Nature B.V; SpringerOpen
• Subjects: Affective computing; Artificial Intelligence; Biological effects; Coding; Cognitive Psychology; Computation by Abstract Devices; Computer Science; Data processing; EEG; Electroencephalography; Emotion recognition; Emotional factors; Emotions; Environmental law; Feature extraction; Health Informatics; Machine Learning Techniques for Neuroscience Big Data; Neurosciences; Physiology; Signal generation; Spatial data; Temporal causal network; China; Temporal causal network; Emotion recognition; EEG
• ISSN: 2198-4018; 2198-4026
• DOI: 10.1186/s40708-024-00245-8

Affective computing is a key research area in computer science, neuroscience, and psychology, aimed at enabling computers to recognize, understand, and respond to human emotional states. As the demand for affective computing technology grows, emotion recognition methods based on physiological signals have become research hotspots. Among these, electroencephalogram (EEG) signals, which reflect brain activity, are highly promising. However, due to individual physiological and anatomical differences, EEG signals introduce noise, reducing emotion recognition performance. Additionally, the synchronous collection of multimodal data in practical applications requires high equipment and environmental standards, limiting the practical use of EEG signals. To address these issues, this study proposes the Emotion Preceptor, a cross-subject emotion recognition model based on unimodal EEG signals. This model introduces a Static Spatial Adapter to integrate spatial information in EEG signals, reducing individual differences and extracting robust encoding information. The Temporal Causal Network then leverages temporal information to extract beneficial features for emotion recognition, achieving precise recognition based on unimodal EEG signals. Extensive experiments on the SEED and SEED-V datasets demonstrate the superior performance of the Emotion Preceptor and validate the effectiveness of the new data processing method that combines DE features in a temporal sequence. Additionally, we analyzed the model’s data flow and encoding methods from a biological interpretability perspective and validated it with neuroscience research related to emotion generation and regulation, promoting further development in emotion recognition research based on EEG signals.