From Segments to Trajectories: Evolving Affective Graphs with Evidence Retrieval for Continuous EEG Emotion Recognition

2026-09-25

AI Summary

This study addresses the limitation of conventional EEG-based emotion recognition, which oversimplifies continuous processes into static segment predictions and struggles to capture dynamic emotional evolution. To this end, we define a full-trial affective trajectory prediction task and propose the EAGER framework. This method introduces a state-guided topology evolution module and a multi-scale temporal evidence retrieval mechanism. By synergistically modeling EEG spatial topological evolution and multi-scale temporal trends through an evolving emotion graph network, EAGER transcends static classification constraints to achieve fine-grained, temporally aligned dynamic emotion tracking. Evaluations on benchmark datasets such as MAHNOB-HCI demonstrate that the proposed approach significantly outperforms existing representative methods in trajectory tracking metrics while maintaining competitive point-wise prediction errors.

Abstract

Electroencephalography (EEG)-based emotion recognition is important for affective computing and human-computer interaction, yet most existing methods divide a long trial into short segments and assign each segment the label of its source trial. Although this strategy increases the number of training samples, it reduces an evolving emotional response to a segment-level, coarse-grained, and static prediction problem. In reality, emotion may continuously emerge, intensify, weaken, and fluctuate as a stimulus unfolds, motivating the prediction of a time-aligned affective trajectory from the complete EEG trial. This task requires coordinated modeling of how spatial neural organization evolves throughout the trial and how local emotional fluctuations interact with longer-term trends. In this work, we formally define and systematically investigate continuous EEG emotion recognition as whole-trial affective trajectory prediction. We propose EAGER, an Evolving Affective Graph framework with Evidence Retrieval for continuous EEG emotion recognition. EAGER comprises two complementary modules: Affective State-guided Topology Evolution models the evolving spatial organization of EEG activity, while Multi-scale Temporal Evidence Retrieval integrates short-term fluctuations with longer-range temporal trends for time-aligned prediction. Experiments on MAHNOB-HCI, SEED-VII, and REFED show consistent gains in trajectory-tracking metrics over representative methods, with competitive pointwise errors.

Problem

• Continuous EEG emotion recognition
• Affective trajectory prediction
• Dynamic emotion modeling
• Whole-trial analysis

Innovation

• Continuous EEG Emotion Recognition
• Affective Trajectory Prediction
• Evolving Affective Graph
• Evidence Retrieval
• Topology Evolution