Whereas most brain-computer interface research has focused on decoding neural signals into behavior or intent, the reverse challenge-using controlled stimuli to steer brain activity-remains far less understood, particularly in the visual domain. However, designing images that consistently elicit desired neural responses is difficult: subjective states lack clear quantitative measures, and EEG feedback is both noisy and non-differentiable. We introduce MindPilot, the first closed-loop framework that uses EEG signals as optimization feedback to guide naturalistic image generation. Unlike prior work limited to invasive settings or low-level flicker stimuli, MindPilot leverages non-invasive EEG with natural images, treating the brain as a black-box function and employing a pseudo-model guidance mechanism to iteratively refine images without requiring explicit rewards or gradients. We validate MindPilot in both simulation and human experiments, demonstrating (i) efficient retrieval of semantic targets, (ii) closed-loop optimization of EEG features, and (iii) human-subject validations in mental matching and emotion regulation tasks. Our results establish the feasibility of EEG-guided image synthesis and open new avenues for non-invasive closed-loop brain modulation, bidirectional brain-computer interfaces, and neural signal-guided generative modeling.

翻译：尽管大多数脑机接口研究专注于将神经信号解码为行为或意图，但反向挑战——使用受控刺激来引导大脑活动——仍然远未得到充分理解，特别是在视觉领域。然而，设计能够持续引发预期神经反应的图像是困难的：主观状态缺乏清晰的量化指标，且脑电图反馈既存在噪声又不可微分。我们提出了MindPilot，这是首个使用脑电图信号作为优化反馈来引导自然图像生成的闭环框架。与先前局限于侵入式设置或低级闪烁刺激的研究不同，MindPilot利用非侵入式脑电图与自然图像，将大脑视为一个黑盒函数，并采用伪模型引导机制来迭代优化图像，而无需明确的奖励或梯度。我们在模拟和人体实验中验证了MindPilot，证明了其能够：（i）有效检索语义目标，（ii）对脑电图特征进行闭环优化，以及（iii）在心理匹配和情绪调节任务中通过人体受试者验证。我们的研究结果确立了脑电图引导图像合成的可行性，并为非侵入式闭环脑调制、双向脑机接口以及神经信号引导的生成建模开辟了新途径。