IEEE Trans Neural Syst Rehabil Eng. 2023;31:218-228. doi: 10.1109/TNSRE.2022.3219418. Epub 2023 Jan 31.
Electroencephalogram (EEG) classification has attracted great attention in recent years, and many models have been presented for this task. Nevertheless, EEG data vary from subject to subject, which may lead to the performance of a classifier degrades due to individual differences. To collect enough labeled data to model would address the issue, but it is often time-consuming and labor-intensive. In this paper, we propose a new multi-source transfer learning method based on domain adversarial neural network for EEG classification. Specifically, we design a domain adversarial neural network, which includes a feature extractor, a classifier, and a domain discriminator, and therefore reduce the domain shift to achieve the purpose. In addition, a unified multi-source optimization framework is constructed to further improve the performance, and the result for EEG classification is induced by the weighted combination of the predictions from multiple source domains. Experiments on three publicly available EEG datasets validate the advantages of the proposed method.
脑电图 (EEG) 分类近年来受到了极大的关注，已经提出了许多用于该任务的模型。然而，EEG 数据因人而异，这可能导致分类器的性能因个体差异而下降。为了解决这个问题，可以收集足够的有标签数据来建模，但这通常是耗时和费力的。在本文中，我们提出了一种新的基于域对抗神经网络的多源迁移学习方法，用于 EEG 分类。具体来说，我们设计了一个域对抗神经网络，它包括一个特征提取器、一个分类器和一个域判别器，从而减少域迁移以达到目的。此外，还构建了一个统一的多源优化框架，以进一步提高性能，通过对多个源域的预测进行加权组合来得到 EEG 分类的结果。在三个公开的 EEG 数据集上的实验验证了所提出方法的优势。
