Brain-computer interfaces (BCIs) are evolving from research prototypes into clinical, assistive, and performance enhancement technologies. Despite the rapid rise and promise of implantable technologies, there is a need for better and more capable wearable and non-invasive approaches whilst also minimising hardware requirements. We present a non-invasive BCI for mind-drawing that iteratively infers a subject's internal visual intent by adaptively presenting visual stimuli (probes) on a screen encoded at different flicker-frequencies and analyses the steady-state visual evoked potentials (SSVEPs). A Gabor-inspired or machine-learned policies dynamically update the spatial placement of the visual probes on the screen to explore the image space and reconstruct simple imagined shapes within approximately two minutes or less using just single-channel EEG data. Additionally, by leveraging stable diffusion models, reconstructed mental images can be transformed into realistic and detailed visual representations. Whilst we expect that similar results might be achievable with e.g. eye-tracking techniques, our work shows that symbiotic human-AI interaction can significantly increase BCI bit-rates by more than a factor 5x, providing a platform for future development of AI-augmented BCI.

翻译：脑机接口正从研究原型逐步发展为临床辅助与性能增强技术。尽管植入式技术发展迅速且前景广阔，但当前仍需在最小化硬件需求的同时，开发性能更优、能力更强的可穿戴与非侵入式方案。本研究提出一种用于思维绘图的非侵入式脑机接口系统，该系统通过在屏幕上编码不同闪烁频率的自适应视觉刺激（探针），并分析稳态视觉诱发电位，迭代推断受试者的内部视觉意图。系统采用Gabor启发式或机器学习策略动态调整屏幕上视觉探针的空间分布，以探索图像空间并重建简单想象图形，仅需单通道脑电数据即可在约两分钟内完成。此外，通过集成稳定扩散模型，重建的心理图像可转化为逼真细致的视觉表征。尽管类似结果可能通过眼动追踪等技术实现，但本研究表明：共生式人机交互能将脑机接口比特率提升5倍以上，为未来人工智能增强型脑机接口的发展提供了技术平台。