标题： 基于EEG融合的数字孪生脑在虚拟场景中的自动驾驶 显示英文标题

标题： EEG-fused Digital Twin Brain for Autonomous Driving in Virtual Scenarios

摘要： 当前的方法通常将生物物理大脑模型与功能性磁共振成像(fMRI)数据相结合——虽然提供了毫米级的空间分辨率（0.5-2 mm^3体素），但这些方法在连续任务中追踪快速神经动态时受到有限的时间分辨率（>0.5 Hz）的限制。 相反，脑电图(EEG)提供了毫秒级的时间精度（<=1 ms采样率），可用于连续任务执行的实时指导，尽管其空间分辨率较低。 为了调和这些互补的模态，我们提出了一种可推广的贝叶斯推断框架，将高空间分辨率的结构磁共振成像(sMRI)与高时间分辨率的EEG相结合，构建一个生物现实的数字孪生大脑(DTB)模型。 该框架建立了毫秒级EEG与sMRI衍生的尖峰网络之间的体素级映射，并通过一个受大脑启发的自动驾驶模拟来展示其转化潜力。 我们的EEG-DTB模型实现了以下能力：(1) 生物学上合理的EEG信号生成（静息态0.88，任务态0.60相关性），任务态的模拟信号在转向预测方面优于随机和经验信号（p<0.05）；(2) 在CARLA模拟器中使用解码的转向角度成功实现自动驾驶。 所提出的方法开创了一种新的范式，用于研究感觉运动整合以及感知-行动循环的机制研究和脑启发控制系统的开发。 显示英文摘要

摘要： Current methodologies typically integrate biophysical brain models with functional magnetic resonance imaging(fMRI) data - while offering millimeter-scale spatial resolution (0.5-2 mm^3 voxels), these approaches suffer from limited temporal resolution (>0.5 Hz) for tracking rapid neural dynamics during continuous tasks. Conversely, Electroencephalogram (EEG) provides millisecond-scale temporal precision (<=1 ms sampling rate) for real-time guidance of continuous task execution, albeit constrained by low spatial resolution. To reconcile these complementary modalities, we present a generalizable Bayesian inference framework that integrates high-spatial-resolution structural MRI(sMRI) with high-temporal-resolution EEG to construct a biologically realistic digital twin brain(DTB) model. The framework establishes voxel-wise mappings between millisecond-scale EEG and sMRI-derived spiking networks, while demonstrating its translational potential through a brain-inspired autonomous driving simulation. Our EEG-DTB model achieves capabilities: (1) Biologically-plausible EEG signal generation (0.88 resting-state,0.60 task-state correlation), with simulated signals in task-state yielding steering predictions outperforming both chance and empirical signals (p<0.05); (2) Successful autonomous driving in the CARLA simulator using decoded steering angles. The proposed approach pioneers a new paradigm for studying sensorimotor integration and for mechanistic studies of perception-action cycles and the development of brain-inspired control systems.