MIND-EEG: Multi-granularity Integration Network with Discrete Codebook for EEG-based Emotion Recognition

TL;DR

This paper introduces MIND-EEG, a novel multi-granularity neural network with a discrete codebook for improved EEG-based emotion recognition, capturing complex brain spatial relationships and enhancing model generalization.

Contribution

The paper proposes a multi-granularity framework with a discrete codebook mechanism to better model brain spatial relationships and improve EEG emotion recognition performance.

Findings

Effective modeling of complex spatial brain interactions.

Enhanced robustness and generalization in emotion recognition.

Superior performance over existing methods.

Abstract

Emotion recognition using electroencephalogram (EEG) signals has broad potential across various domains. EEG signals have ability to capture rich spatial information related to brain activity, yet effectively modeling and utilizing these spatial relationships remains a challenge. Existing methods struggle with simplistic spatial structure modeling, failing to capture complex node interactions, and lack generalizable spatial connection representations, failing to balance the dynamic nature of brain networks with the need for discriminative and generalizable features. To address these challenges, we propose the Multi-granularity Integration Network with Discrete Codebook for EEG-based Emotion Recognition (MIND-EEG). The framework employs a multi-granularity approach, integrating global and regional spatial information through a Global State Encoder, an Intra-Regional Functionality Encoder, and an Inter-Regional Interaction Encoder to comprehensively model brain activity. Additionally, we introduce a discrete codebook mechanism for constructing network structures via vector quantization, ensuring compact and meaningful brain network representations while mitigating over-smoothing and enhancing model generalization. The proposed framework effectively captures the dynamic and diverse nature of EEG signals, enabling robust emotion recognition. Extensive comparisons and analyses demonstrate the effectiveness of MIND-EEG, and the source code is publicly available at https://anonymous.4open.science/r/MIND_EEG.