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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

Abstract

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.

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

Abstract

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.
Paper Structure (58 sections, 14 equations, 7 figures, 10 tables)

This paper contains 58 sections, 14 equations, 7 figures, 10 tables.

Table of Contents

  1. Introduction
  2. Synthetic Data Generation for BCIs
  3. Knowledge-Based Generation
  4. Feature-Based Generation
  5. Model-Based Generation
  6. Generative Adversarial Networks
  7. Variational Autoencoders
  8. Autoregressive Models
  9. Denoising Diffusion Probabilistic Models
  10. Translation-Based Generation
  11. Joint Latent Space Generation
  12. Conditional Latent Space Generation
  13. Comparison of Generation Approaches
  14. Benchmark Experiments
  15. Dataset
  16. ...and 43 more sections

Figures (7)

  • Figure 1: Data scarcity issue in BCIs, owing to the small data size, low signal quality, and/or significant differences across sessions, subjects, and devices.
  • Figure 2: Data generation driven machine learning pipeline for BCIs, which includes brain signal acquisition, data preprocessing, data generation, feature engineering, and classification/regression. The latter two components can be unified into a single end-to-end neural network. Data generation approaches are categorized into four types: (a) knowledge-based generation, (b) feature-based generation, (c) model-based generation, and (d) translation-based generation.
  • Figure 3: Four types of data generation approaches for brain signals.
  • Figure 4: Visualizations of brain signals before (blue lines) and after (red lines) eleven knowledge-based generation approaches, using 1-channel as an example.
  • Figure 5: Model-based generation approaches for brain signals, including GANS, VAEs, AMs, and DDPMs.
  • ...and 2 more figures