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.
Abstract (translated)
利用脑电图（EEG）信号进行自动情感识别已经引起了大量关注。虽然深度学习方法表现出强大的性能，但它们通常容易受到各种扰动的影响，比如环境噪声和攻击性扰动。在本文中，我们提出了一种Inception特征生成器和双侧扰动（INC-TSP）方法，以增强脑机接口中的情感识别。INC-TSP将Inception模块与EEG数据分析相结合，并使用双侧扰动（TSP）作为防御措施来对抗输入扰动。TSP将模型的权重和输入引入最坏情况扰动，增强了模型对攻击性扰动的弹性。所提出的方法解决了在输入不确定性的存在下保持准确情感识别的挑战。我们在一个独立于受试者的三分类情感识别场景中验证了INC-TSP，证明了其稳健性能。
URL
https://arxiv.org/abs/2404.15373
