.png){kind=icon}
Return
Graph-Enhanced Emotion Neural Decoding
.png){kind=icon}
PRE
AI
DOI：10.1109/TMI.2023.3246220.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Brain signal-based emotion recognition has recently attracted considerable attention since it has powerful potential to be applied in human-computer interaction. To realize the emotional interaction of intelligent systems with humans, researchers have made efforts to decode human emotions from brain imaging data. The majority of current efforts use emotion similarities (e.g., emotion graphs) or brain region similarities (e.g., brain networks) to learn emotion and brain representations. However, the relationships between emotions and brain regions are not explicitly incorporated into the representation learning process. As a result, the learned representations may not be informative enough to benefit specific tasks, e.g., emotion decoding. In this work, we propose a novel idea of graph-enhanced emotion neural decoding, which takes advantage of a bipartite graph structure to integrate the relationships between emotions and brain regions into the neural decoding process, thus helping learn better representations. Theoretical analyses conclude that the suggested emotion-brain bipartite graph inherits and generalizes the conventional emotion graphs and brain networks. Comprehensive experiments on visually evoked emotion datasets demonstrate the effectiveness and superiority of our approach.
Keywords:
Brain region
emotion
graph neural networks
neural decoding
representation
Journal
IF:
9.8
Papers:
6.3K
Citations:
3.7W
Organization
Cited Papers
.png){kind=icon}
The Cortical Network of Emotion Regulation: Insights From Advanced EEG-fMRI Integration Analysis
PREAI
ShareSavePreparation, characterization, and structure of half gap junctional layers split with urea and EGTA
PREAI
ShareSave
View more