Robust EEG-based Emotion Recognition Using an Inception and Two-sided Perturbation Model

arXiv:2404.15373v1 Announce Type: cross
Abstract: 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 …

abstract adversarial adversarial attacks arxiv attacks attention automated cs.ai cs.lg deep learning eeg eess.sp emotion environmental feature generator noise paper performance recognition robust type vulnerabilities