A Multi-Scale Temporal Framework with Dynamic Fusion for EEG-Based Emotion Recognition

2026-08-09

AI Summary

This study addresses the limitations of existing EEG-based emotion recognition methods, which typically rely on a single time window and struggle to capture the multi-scale temporal dynamics inherent in complex emotional states such as mixed emotions. To overcome this, the authors propose a multi-scale temporal modeling framework that decomposes EEG signals into multiple time windows, extracts features using a shared attention-based encoder, and employs a dynamic fusion module to adaptively assign sample-specific weights across scales. The approach is the first to demonstrate the effectiveness of multi-scale modeling in a three-class emotion recognition task involving mixed emotions. Under a subject-independent protocol, the method achieves classification accuracies of 65.22% for binary classification and 45.43% for ternary classification, significantly outperforming baseline models using the full-length signal.

Abstract

Mixed emotions represent a clinically relevant but still underexplored target for automatic emotion recognition. EEG provides millisecond-level access to neural activity, yet most EEG pipelines analyze the signal through a single temporal window, thereby fixing the temporal structure available to the model. This study introduces a multi-scale temporal framework for EEG-based emotion recognition. The EEG waveform is decomposed into windows of one or several durations, processed by a shared attention-based encoder, and integrated through a dynamic fusion module that assigns sample-specific weights across temporal scales. The framework is evaluated under a subject-independent protocol in binary and three-class settings, with the three-class task including the mixed affective category. The best results are 65.22% for the two-class task and 45.43% for the three-class task. Both are obtained with three-scale dynamic-fusion configurations and remain substantially above the full-signal baseline. The best-performing temporal scales differ between the two tasks. Dynamic fusion outperforms concatenation in the highest-scoring two-class configuration and slightly exceeds it in the highest-scoring three-class configuration, although these multi-scale settings require substantially more computation than the full-signal baseline.

Problem

• EEG-based emotion recognition
• mixed emotions
• multi-scale temporal analysis
• temporal window
• subject-independent protocol

Innovation

• multi-scale temporal framework
• dynamic fusion
• EEG-based emotion recognition
• mixed emotions
• attention-based encoder