Hansen Sofie Therese, Hauberg Søren, Hansen Lars Kai
Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 324, DK-2800 Kgs. Lyngby, Denmark.
Neuroimage. 2016 Oct 1;139:249-258. doi: 10.1016/j.neuroimage.2016.06.017. Epub 2016 Jun 13.
Electroencephalography (EEG) is a flexible and accessible tool with excellent temporal resolution but with a spatial resolution hampered by volume conduction. Reconstruction of the cortical sources of measured EEG activity partly alleviates this problem and effectively turns EEG into a brain imaging device. The quality of the source reconstruction depends on the forward model which details head geometry and conductivities of different head compartments. These person-specific factors are complex to determine, requiring detailed knowledge of the subject's anatomy and physiology. In this proof-of-concept study, we show that, even when anatomical knowledge is unavailable, a suitable forward model can be estimated directly from the EEG. We propose a data-driven approach that provides a low-dimensional parametrization of head geometry and compartment conductivities, built using a corpus of forward models. Combined with only a recorded EEG signal, we are able to estimate both the brain sources and a person-specific forward model by optimizing this parametrization. We thus not only solve an inverse problem, but also optimize over its specification. Our work demonstrates that personalized EEG brain imaging is possible, even when the head geometry and conductivities are unknown.
脑电图（EEG）是一种灵活且易于使用的工具，具有出色的时间分辨率，但空间分辨率受容积传导的影响。测量到的EEG活动的皮质源重建部分缓解了这个问题，并有效地将EEG转变为一种脑成像设备。源重建的质量取决于详细描述头部几何形状和不同头部区域电导率的正向模型。这些因人而异的因素很难确定，需要详细了解受试者的解剖结构和生理状况。在这项概念验证研究中，我们表明，即使无法获取解剖学知识，也可以直接从EEG估计出合适的正向模型。我们提出了一种数据驱动的方法，该方法使用正向模型库对头几何形状和区域电导率进行低维参数化。仅结合记录的EEG信号，我们就能够通过优化此参数化来估计脑源和因人而异的正向模型。因此，我们不仅解决了一个逆问题，还对其规范进行了优化。我们的工作表明，即使头部几何形状和电导率未知，个性化的EEG脑成像也是可行的。
