  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.
