Individual differences of Electroencephalogram (EEG) could cause the domain shift which would significantly degrade the performance of cross-subject strategy. The domain adversarial neural networks (DANN), where the classification loss and domain loss jointly update the parameters of feature extractor, are adopted to deal with the domain shift. However, limited EEG data quantity and strong individual difference are challenges for the DANN with cumbersome feature extractor. In this work, we propose knowledge distillation (KD) based lightweight DANN to enhance cross-subject EEG-based emotion recognition. Specifically, the teacher model with strong context learning ability is utilized to learn complex temporal dynamics and spatial correlations of EEG, and robust lightweight student model is guided by the teacher model to learn more difficult domain-invariant features. In the feature-based KD framework, a transformer-based hierarchical temporalspatial learning model is served as the teacher model. The student model, which is composed of Bi-LSTM units, is a lightweight version of the teacher model. Hence, the student model could be supervised to mimic the robust feature representations of teacher model by leveraging complementary latent temporal features and spatial features. In the DANN-based cross-subject emotion recognition, we combine the obtained student model and a lightweight temporal-spatial feature interaction module as the feature extractor. And the feature aggregation is fed to the emotion classifier and domain classifier for domain-invariant feature learning. To verify the effectiveness of the proposed method, we conduct the subject-independent experiments on the public dataset DEAP with arousal and valence classification. The outstanding performance and t-SNE visualization of latent features verify the advantage and effectiveness of the proposed method.

翻译：脑电图（EEG）的个体差异会导致域偏移，从而显著降低跨被试策略的性能。域对抗神经网络（DANN）通过分类损失和域损失联合更新特征提取器的参数，被用于处理域偏移问题。然而，有限的脑电图数据量和强烈的个体差异对具有复杂特征提取器的DANN构成了挑战。本文提出基于知识蒸馏（KD）的轻量级DANN，以增强跨被试脑电图情绪识别。具体而言，利用具有强大上下文学习能力的教师模型学习脑电图复杂的时域动态和空间相关性，并由教师模型引导鲁棒的轻量级学生模型学习更具挑战性的域不变特征。在基于特征的知识蒸馏框架中，基于Transformer的层次化时空学习模型作为教师模型。学生模型由Bi-LSTM单元组成，是教师模型的轻量级版本。因此，学生模型可以通过利用互补的潜在时域特征和空间特征，被监督学习以模仿教师模型的鲁棒特征表示。在基于DANN的跨被试情绪识别中，我们将所得学生模型与轻量级时空特征交互模块相结合作为特征提取器，并将特征聚合结果输入情绪分类器和域分类器，以实现域不变特征学习。为验证所提方法的有效性，我们在公开数据集DEAP上进行了基于唤醒度和效价分类的被试独立实验。优异的性能表现和潜在特征的t-SNE可视化验证了所提方法的优势和有效性。