Currently, most models rarely consider the negative transfer problem in the research field of cross-subject EEG emotion recognition. To solve this problem, this paper proposes a semi-supervised domain adaptive algorithm based on few labeled samples of target subject, which called multi-domain geodesic flow kernel dynamic distribution alignment (MGFKD). It consists of three modules: 1) GFK common feature extractor: projects the feature distribution of source and target subjects to the Grassmann manifold space, and obtains the latent common features of the two feature distributions through GFK method. 2) Source domain selector: obtains pseudo-labels of the target subject through weak classifier, finds "golden source subjects" by using few known labels of target subjects. 3) Label corrector: uses a dynamic distribution balance strategy to correct the pseudo-labels of the target subject. We conducted comparison experiments on the SEED and SEED-IV datasets, and the results show that MGFKD outperforms unsupervised and semi-supervised domain adaptation algorithms, achieving an average accuracy of 87.51±7.68% and 68.79±8.25% on the SEED and SEED-IV datasets with only one labeled sample per video for target subject. Especially when the number of source domains is set as 6 and the number of known labels is set as 5, the accuracy increase to 90.20±7.57% and 69.99±7.38%, respectively. The above results prove that our proposed algorithm can efficiently and quickly improve the cross-subject EEG emotion classification performance. Since it only need a small number of labeled samples of new subjects, making it has strong application value in future EEG-based emotion recognition applications.

中文翻译：

---

MGFKD：一种用于跨主体脑电情感识别的半监督多源域自适应算法

目前，在跨学科脑电情感识别研究领域，大多数模型很少考虑负迁移问题。针对这一问题，提出一种基于目标主体少量标记样本的半监督域自适应算法，即多域测地流核动态分布对齐（MGFKD）。它由三个模块组成： 1）GFK公共特征提取器：将源和目标主体的特征分布投影到Grassmann流形空间，并通过GFK方法获得两个特征分布的潜在公共特征。 2）源域选择器：通过弱分类器获得目标主题的伪标签，利用目标主题的少数已知标签找到“黄金源主题”。 3）标签校正器：采用动态分布平衡策略来校正目标主体的伪标签。我们在 SEED 和 SEED-IV 数据集上进行了对比实验，结果表明 MGFKD 优于无监督和半监督域自适应算法，在 SEED 和 SEED-IV 上实现了 87.51±7.68% 和 68.79±8.25% 的平均准确率目标主题的每个视频只有一个标记样本的数据集。特别是当源域的数量设置为6和已知标签的数量设置为5时，准确率分别提高到90.20±7.57%和69.99±7.38%。上述结果证明我们提出的算法可以有效、快速地提高跨主题脑电情感分类性能。由于它只需要少量新受试者的标记样本，使得它在未来基于脑电图的情感识别应用中具有很强的应用价值。