EEG-fused Digital Twin Brain for Autonomous Driving in Virtual Scenarios
Yubo Hou, Zhengxin Zhang, Ziyi Wang, Wenlian Lu, Jianfeng Feng, Taiping Zeng

TL;DR
This paper introduces a Bayesian framework that fuses EEG and MRI data to create a digital twin brain model, enabling real-time autonomous driving simulation and advancing understanding of sensorimotor integration.
Contribution
It presents a novel method for integrating high-temporal EEG with high-spatial MRI data to build a biologically realistic digital twin brain for autonomous driving.
Findings
Achieved biologically plausible EEG signal generation with high correlation to real data.
Demonstrated successful autonomous driving in a simulated environment using decoded steering signals.
Outperformed chance and empirical signals in steering prediction accuracy.
Abstract
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…
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Taxonomy
TopicsEEG and Brain-Computer Interfaces · Robotics and Automated Systems · Digital Transformation in Industry
