Continuous eeg emotion tracking improves understanding of changing feelings
From Segments to Trajectories: Evolving Affective Graphs with Evidence Retrieval for Continuous EEG Emotion Recognition
Human-Computer Interaction
Summary
Most emotion recognition from brain waves breaks long recordings into short parts and guesses emotion for each part independently. The authors point out that emotions actually change continuously over time as we experience something. They propose a new method called EAGER that looks at the whole brain activity over the entire recording, modeling how brain connections and emotion signals evolve. This helps predict a smooth, detailed path of emotions instead of isolated snapshots. Their method performs better on several emotion datasets than previous approaches.
What this means in practice
- •For affective computing developers: Improve emotion-aware applications by providing detailed continuous emotion predictions from EEG signals during long stimuli.
- •For human-computer interaction teams: Use evolving EEG-based emotion trajectories to create more responsive systems adapting in real-time to users' emotional states.
Authors
Chi Yang, Jihong Wang, Chengxi Xie, Kai He, Huan Liu, Man Yao, Shile Qi, Yuzhe Zhang
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.