FDC-Net: Rethinking the association between EEG artifact removal and multi-dimensional affective computing

TL;DR

This paper introduces FDC-Net, a novel end-to-end framework that jointly optimizes EEG artifact removal and emotion recognition, significantly improving robustness and accuracy in affective computing applications.

Contribution

FDC-Net is the first to deeply couple denoising and emotion recognition tasks via bidirectional learning and attention mechanisms, enhancing noise robustness and performance.

Findings

FDC-Net achieves up to 96.30% correlation coefficient in denoising.

Emotion recognition accuracy reaches 82.3% on DEAP and 88.1% on DREAMER.

Outperforms nine state-of-the-art methods in artifact removal and emotion recognition.

Abstract

Electroencephalogram (EEG)-based emotion recognition holds significant value in affective computing and brain-computer interfaces. However, in practical applications, EEG recordings are susceptible to the effects of various physiological artifacts. Current approaches typically treat denoising and emotion recognition as independent tasks using cascaded architectures, which not only leads to error accumulation, but also fails to exploit potential synergies between these tasks. Moreover, conventional EEG-based emotion recognition models often rely on the idealized assumption of "perfectly denoised data", lacking a systematic design for noise robustness. To address these challenges, a novel framework that deeply couples denoising and emotion recognition tasks is proposed for end-to-end noise-robust emotion recognition, termed as Feedback-Driven Collaborative Network for Denoising-Classification Nexus (FDC-Net). Our primary innovation lies in establishing a dynamic collaborative mechanism between artifact removal and emotion recognition through: (1) bidirectional gradient propagation with joint optimization strategies; (2) a gated attention mechanism integrated with frequency-adaptive Transformer using learnable band-position encoding. Two most popular EEG-based emotion datasets (DEAP and DREAMER) with multi-dimensional emotional labels were employed to compare the artifact removal and emotion recognition performance between FDC-Net and nine state-of-the-art methods. In terms of the denoising task, FDC-Net obtains a maximum correlation coefficient (CC) value of 96.30% on DEAP and a maximum CC value of 90.31% on DREAMER. In terms of the emotion recognition task under physiological artifact interference, FDC-Net achieves emotion recognition accuracies of 82.3+7.1% on DEAP and 88.1+0.8% on DREAMER.