Multi-modal Mood Reader: Pre-trained Model Empowers Cross-Subject Emotion Recognition

Dong, Yihang; Chen, Xuhang; Shen, Yanyan; Ng, Michael Kwok-Po; Qian, Tao; Wang, Shuqiang

doi:10.1007/978-981-97-7007-6_13

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Book1

Other1

Article1

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

MedPrompt: Cross-modal Prompting for Multi-task Medical Image Translation

Chen,

Luo,

Pun

et al. 2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

MedPrompt: Cross-modal Prompting for Multi-task Medical Image Translation

Chen,

Luo,

Pun

et al. 2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

A No-Reference Image Quality Assessment Method Based on Random Forest for Power Scenario

Li,

Zeng,

Kong

et al. 2024

2024 3rd International Conference on Artificial Intelligence and Computer Information Technology (AICIT)

View full text Add to dashboard Cite

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

Dong,

Jing,

Mahmud

et al. 2024

Brain Inf.

View full text Add to dashboard Cite

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.

show abstract

Multi-modal Mood Reader: Pre-trained Model Empowers Cross-Subject Emotion Recognition

Cited by 3 publications

References 43 publications

MedPrompt: Cross-modal Prompting for Multi-task Medical Image Translation

MedPrompt: Cross-modal Prompting for Multi-task Medical Image Translation

A No-Reference Image Quality Assessment Method Based on Random Forest for Power Scenario

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

Contact Info

Product

Resources

About