Adversarial Adaptation Neural Networks With Class-Informed Discriminator for EEG Emotion Recognition

Individual differences and nonstationary characteristics are prominent in electroencephalography (EEG) signals. Therefore, aligning the source and target domain data becomes essential in cross-subject and cross-session classification tasks. Although many adversarial adaptation networks can achieve distribution alignment through domain-level adaptation, they tend to disregard the multimodal structure inherent in the data. In this article, we present a concise and effective adversarial paradigm for EEG emotion recognition. This approach fully utilizes the label structure information of source domain data to reuse the binary discriminator as a class-informed discriminator instead of introducing additional modules, which not only realizes domain confusion but also ensures that mode information is retained in the process of confusion to avoid mode collapse. To evaluate our method, a systematic experimental study was conducted on the public datasets SEED and SEED-IV. The average accuracy of cross-subject and cross-session scenarios achieved 90.21%, 95.47% on SEED, and 77.50%, 77.54% on SEED-IV, respectively. Compared to the existing domain adaptation methods, the evident improvements of classification performance demonstrate the feasibility and effectiveness of our method.