EEG-fused Digital Twin Brain for Autonomous Driving in Virtual Scenarios
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915393510047744 |
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| author | Hou, Yubo Zhang, Zhengxin Wang, Ziyi Lu, Wenlian Feng, Jianfeng Zeng, Taiping |
| author_facet | Hou, Yubo Zhang, Zhengxin Wang, Ziyi Lu, Wenlian Feng, Jianfeng Zeng, Taiping |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_12263 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EEG-fused Digital Twin Brain for Autonomous Driving in Virtual Scenarios Hou, Yubo Zhang, Zhengxin Wang, Ziyi Lu, Wenlian Feng, Jianfeng Zeng, Taiping Neurons and Cognition 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. |
| title | EEG-fused Digital Twin Brain for Autonomous Driving in Virtual Scenarios |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2507.12263 |