MindPilot: Closed-loop Visual Stimulation Optimization for Brain Modulation with EEG-guided Diffusion

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. Recent advances in controllable generation, particularly text-conditioned diffusion models (Li et al., 2019; Rahmani et al., 2022; Epstein et al., 2023; Wei et al., 2024a), offer unprecedented flexibility in image synthesis. But these models are optimized for linguistic prompts, not neural feedback, and thus remain orthogonal to the challenge of brain-targeted generation. Prior efforts in closed-loop visual neuromodulation have shown that generative models can be guided by neuronal responses to synthesize activity-maximizing stimuli (