AI 中文总结
该研究提出基于混合体积-表面-导线积分公式的EEG源成像框架，可实现白质各向异性贡献的神经束感知建模，与FEM参考解吻合度高，兼具鲁棒定位性能与交互式可视化能力。
AI 中文摘要
在EEG源成像中，脑活动成像的精度取决于正向头模型的准确性，而正向头模型又受组织电导率表征的影响，包括颅骨和白质等各向异性区域。我们提出一种基于混合体积-表面-导线积分公式的EEG源成像框架，该框架无需对头部进行全体积离散化即可实现对白质各向异性贡献的神经束感知建模。我们在基于真实MRI的头部解剖结构上对所提框架进行评估，结果显示其与有限元法（FEM）参考解吻合度高，同时避免了全体积网格划分，且能自然表征白质神经束。随后我们将该模型集成到将EEG处理与沉浸式可视化耦合的实时管线中，实现了头皮电位、皮层源和白质神经束相关量的同步查看。结果表明，所提公式是经典公式的神经束感知替代方案，兼具鲁棒的定位性能与交互式可视化能力。
英文摘要
In EEG source imaging, the precision of the imaging of the brain activity depends on the accuracy of the forward head model which is, in turn, affected by the representation of tissue conductivity, including anisotropic compartments such as the skull and white matter. We introduce an EEG source imaging framework based on a hybrid volume-surface-wire integral formulation, enabling tract-aware modeling of anisotropic white matter contributions without a full volumetric head discretization. The proposed framework is assessed on a realistic MRI-derived head anatomy, showing close agreement with FEM reference solutions while avoiding full-volume meshing and providing a natural representation of white matter fiber tracts. We then integrate this model into a real-time pipeline that couples EEG processing with immersive visualization, enabling synchronized inspection of scalp potentials, cortical sources, and white matter fiber tracts-related quantities. The results support the proposed formulation as a tract-aware alternative to canonical formulations, combining robust localization performance with interactive visualization capabilities.
Comments15 pages, 7 figures
