Synthetic Data Generation for Brain-Computer Interfaces: Overview, Benchmarking, and Future Directions
It tackles the limited, heterogeneous, and privacy-sensitive neural data issue for BCI researchers, but is incremental as a survey and benchmarking effort.
This survey addresses the data scarcity problem in brain-computer interfaces (BCIs) by reviewing and benchmarking synthetic brain signal generation methods, categorizing them into four types and providing a public codebase for objective performance comparisons across four BCI paradigms.
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 https://github.com/wzwvv/DG4BCI.