Synthetic Data Generation for Brain-Computer Interfaces: Overview, Benchmarking, and Future Directions

Computer Science > Machine Learning [Submitted on 11 Mar 2026] Synthetic Data Generation for Brain-Computer Interfaces: Overview, Benchmarking, and Future Directions Ziwei Wang, Zhentao He, Xingyi He, Hongbin Wang, Tianwang Jia, Jingwei Luo, Siyang Li, Xiaoqing Chen, Dongrui Wu Deep learning has achieved transformative performance across diverse domains, largely driven by the large-scale, high-quality training data. In contrast, the development of brain-computer interfaces (BCIs) is fundamentally constrained by the limited, heterogeneous, and privacy-sensitive neural recordings. Generating synthetic yet physiologically plausible brain signals has therefore emerged as a compelling way 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. We systematically categorize existing generative algorithms into four types: knowledge-based, feature-based, model-based, and translation-based approaches. Furthermore, we benchmark existing brain signal generation approaches across four representative BCI paradigms to provide an objective performance comparison. Finally, we discuss the potentials and challenges of current generation approaches and prospect future research on accurate, data-efficient, and privacy-aware BCI systems. The benchmark codebase is publicized at this https URL. Comments: 20 pages, 7 figures Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Signal Processing (eess.SP) Cite as: arXiv:2603.12296 [cs.LG]   (or arXiv:2603.12296v1 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2603.12296 Focus to learn more Submission history From: Ziwei Wang [view email] [v1] Wed, 11 Mar 2026 20:36:02 UTC (21,407 KB) Access Paper: HTML (experimental) view license Current browse context: cs.LG < prev   |   next > new | recent | 2026-03 Change to browse by: cs cs.AI eess eess.SP References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)