基于掩蔽生成对抗网络和神经架构搜索的脑电图情感识别

Abstract EEG signals face challenges such as a scarcity of training data, significant individual variability, and difficulties in designing neural network architectures, which severely limit the performance and generalization capabilities of emotion recognition models. This paper proposes an EEG-based emotion recognition method that integrates masked generative adversarial data augmentation with personalized neural architecture search. We designed a data augmentation strategy using a frequency-domain feature-based stochastic electrode mask generative adversarial network, which effectively expa