Despite the excellent capabilities of machine learning algorithms, their performance deteriorates when the distribution of test data differs from the distribution of training data. In medical data research, this problem is exacerbated by its connection to human health, expensive equipment, and meticulous setups. Consequently, achieving domain generalizations and domain adaptations under distribution shifts is an essential step in the analysis of medical data. As the first systematic review of domain generalization and domain adaptation on functional brain signals, the article discusses and categorizes various methods, tasks, and datasets in this field. Moreover, it discusses relevant directions for future research.

尽管机器学习算法具有出色的能力，但当测试数据的分布与训练数据的分布不同时，其性能会下降。在医学数据研究中，由于其与人类健康的关系、昂贵的设备和细致的设置，这个问题变得更加严重。因此，在分布变化下实现领域泛化和领域适应是医学数据分析的重要步骤。作为对功能性脑信号领域泛化和领域适应的第一篇系统综述，本文讨论并分类了该领域的各种方法、任务和数据集。此外，还讨论了未来研究的相关方向。