Robust EEG-based Emotion Recognition Using an Inception and Two-sided Perturbation Model
This work addresses the challenge of maintaining accurate emotion recognition in brain-computer interfaces against perturbations like noise and adversarial attacks, representing an incremental improvement.
The paper tackled the problem of robust emotion recognition from EEG signals by proposing an Inception feature generator and two-sided perturbation model, achieving robust performance in a subject-independent three-class scenario.
Automated emotion recognition using electroencephalogram (EEG) signals has gained substantial attention. Although deep learning approaches exhibit strong performance, they often suffer from vulnerabilities to various perturbations, like environmental noise and adversarial attacks. In this paper, we propose an Inception feature generator and two-sided perturbation (INC-TSP) approach to enhance emotion recognition in brain-computer interfaces. INC-TSP integrates the Inception module for EEG data analysis and employs two-sided perturbation (TSP) as a defensive mechanism against input perturbations. TSP introduces worst-case perturbations to the model's weights and inputs, reinforcing the model's elasticity against adversarial attacks. The proposed approach addresses the challenge of maintaining accurate emotion recognition in the presence of input uncertainties. We validate INC-TSP in a subject-independent three-class emotion recognition scenario, demonstrating robust performance.