Feature Fusion Based on Mutual-Cross-Attention Mechanism for EEG Emotion Recognition

TL;DR

This paper introduces a novel Mutual-Cross-Attention feature fusion mechanism combined with a 3D-CNN for EEG-based emotion recognition, achieving high accuracy and interpretability.

Contribution

The paper proposes a new Mutual-Cross-Attention mechanism for effective feature fusion in EEG emotion recognition, enhancing accuracy and interpretability over existing methods.

Findings

Achieved over 99% accuracy on DEAP dataset for valence and arousal.

Demonstrated the effectiveness of the MCA mechanism in discovering feature relationships.

Enhanced model interpretability with the new feature fusion approach.

Abstract

An objective and accurate emotion diagnostic reference is vital to psychologists, especially when dealing with patients who are difficult to communicate with for pathological reasons. Nevertheless, current systems based on Electroencephalography (EEG) data utilized for sentiment discrimination have some problems, including excessive model complexity, mediocre accuracy, and limited interpretability. Consequently, we propose a novel and effective feature fusion mechanism named Mutual-Cross-Attention (MCA). Combining with a specially customized 3D Convolutional Neural Network (3D-CNN), this purely mathematical mechanism adeptly discovers the complementary relationship between time-domain and frequency-domain features in EEG data. Furthermore, the new designed Channel-PSD-DE 3D feature also contributes to the high performance. The proposed method eventually achieves 99.49% (valence) and 99.30% (arousal) accuracy on DEAP dataset.