IEEE Trans Neural Syst Rehabil Eng. 2023;31:2767-2777. doi: 10.1109/TNSRE.2023.3285309. Epub 2023 Jun 27.
Due to the individual difference, EEG signals from other subjects (source) can hardly be used to decode the mental intentions of the target subject. Although transfer learning methods have shown promising results, they still suffer from poor feature representation or neglect long-range dependencies. In light of these limitations, we propose Global Adaptive Transformer (GAT), an domain adaptation method to utilize source data for cross-subject enhancement. Our method uses parallel convolution to capture temporal and spatial features first. Then, we employ a novel attention-based adaptor that implicitly transfers source features to the target domain, emphasizing the global correlation of EEG features. We also use a discriminator to explicitly drive the reduction of marginal distribution discrepancy by learning against the feature extractor and the adaptor. Besides, an adaptive center loss is designed to align the conditional distribution. With the aligned source and target features, a classifier can be optimized to decode EEG signals. Experiments on two widely used EEG datasets demonstrate that our method outperforms state-of-the-art methods, primarily due to the effectiveness of the adaptor. These results indicate that GAT has good potential to enhance the practicality of BCI.
由于个体差异，很难使用来自其他受试者（源）的 EEG 信号来解码目标受试者的心理意图。尽管迁移学习方法已经显示出了有希望的结果，但它们仍然存在特征表示不佳或忽略长距离依赖的问题。针对这些限制，我们提出了全局自适应转换器（GAT），这是一种利用源数据进行跨主体增强的域自适应方法。我们的方法首先使用并行卷积来捕获时间和空间特征。然后，我们采用一种新颖的基于注意力的适配器，将源特征隐式地转移到目标域，强调 EEG 特征的全局相关性。我们还使用鉴别器通过学习特征提取器和适配器来显式地驱动边缘分布差异的减少。此外，设计了自适应中心损失来对齐条件分布。通过使用对齐的源和目标特征，可以优化分类器来解码 EEG 信号。在两个广泛使用的 EEG 数据集上的实验表明，我们的方法优于最先进的方法，主要是由于适配器的有效性。这些结果表明，GAT 具有增强 BCI 实用性的潜力。
