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

TL;DR

This paper introduces INC-TSP, a novel EEG emotion recognition method combining Inception features and two-sided perturbation to improve robustness against noise and adversarial attacks in brain-computer interfaces.

Contribution

It proposes a new model integrating Inception modules with two-sided perturbation to enhance EEG-based emotion recognition robustness.

Findings

Demonstrates improved accuracy under perturbations

Shows robustness against adversarial attacks

Validates effectiveness in subject-independent scenarios

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 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.