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

2026-02-11

AI Summary

This work proposes the first closed-loop framework for natural image generation guided by non-invasive electroencephalography (EEG) feedback, addressing key challenges such as high noise levels, non-differentiability of EEG signals, and the difficulty of quantifying subjective mental states. Treating the brain as a black-box function, the method integrates diffusion-based generative models with EEG feature extraction and employs a pseudo-model-guided mechanism to iteratively refine generated images—enabling targeted modulation of brain activity without explicit rewards or gradient computation. Validated through both simulated and human experiments, the system successfully achieves semantic target retrieval and EEG feature optimization, demonstrating efficacy in tasks involving perceptual alignment and emotional regulation. This study establishes the first end-to-end brain-controlled image generation pipeline based solely on non-invasive EEG signals.

Technology Category

• Humans and AI: Brain-Sensing and Analysis
• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Generation

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• brain modulation
• visual stimulation
• EEG
• closed-loop
• neural response

Innovation

• closed-loop brain modulation
• EEG-guided image generation
• non-invasive BCI
• pseudo-model guidance
• neural signal-driven diffusion