📄 中文摘要
深度学习在多个领域取得了变革性的性能，主要得益于大规模、高质量的训练数据。然而，脑机接口（BCI）的发展受到有限、异质和隐私敏感的神经记录的根本限制。因此，生成合成且生理上合理的脑信号成为缓解数据稀缺和增强模型能力的有效方法。该研究提供了对脑信号生成的全面回顾，涵盖了方法学分类、基准实验、评估指标和关键应用。现有生成算法被系统地分类为四种类型：基于知识的、特征的、模型驱动的和混合方法。通过对这些方法的比较和评估，提出了未来研究的方向和潜在应用。
脑机接口的合成数据生成：概述、基准测试与未来方向
📄 English Summary
Synthetic Data Generation for Brain-Computer Interfaces: Overview, Benchmarking, and Future Directions
Deep learning has achieved transformative performance across various domains, primarily driven by large-scale, high-quality training data. The development of brain-computer interfaces (BCIs) is fundamentally constrained by the limited, heterogeneous, and privacy-sensitive nature of neural recordings. Generating synthetic yet physiologically plausible brain signals has emerged as a compelling solution to mitigate data scarcity and enhance model capacity. This survey provides a comprehensive review of brain signal generation for BCIs, covering methodological taxonomies, benchmark experiments, evaluation metrics, and key applications. Existing generative algorithms are systematically categorized into four types: knowledge-based, feature-based, model-driven, and hybrid methods. By comparing and evaluating these approaches, future research directions and potential applications are proposed.