Ding Lei
School of Electrical and Computer Engineering, University of Oklahoma, 202 W Boyd Street, Carson Engineering Center, Norman, OK 73019, USA.
Phys Med Biol. 2009 May 7;54(9):2683-97. doi: 10.1088/0031-9155/54/9/006. Epub 2009 Apr 8.
In the present study, we have developed a novel electromagnetic source imaging approach to reconstruct extended cortical sources by means of cortical current density (CCD) modeling and a novel EEG imaging algorithm which explores sparseness in cortical source representations through the use of L1-norm in objective functions. The new sparse cortical current density (SCCD) imaging algorithm is unique since it reconstructs cortical sources by attaining sparseness in a transform domain (the variation map of cortical source distributions). While large variations are expected to occur along boundaries (sparseness) between active and inactive cortical regions, cortical sources can be reconstructed and their spatial extents can be estimated by locating these boundaries. We studied the SCCD algorithm using numerous simulations to investigate its capability in reconstructing cortical sources with different extents and in reconstructing multiple cortical sources with different extent contrasts. The SCCD algorithm was compared with two L2-norm solutions, i.e. weighted minimum norm estimate (wMNE) and cortical LORETA. Our simulation data from the comparison study show that the proposed sparse source imaging algorithm is able to accurately and efficiently recover extended cortical sources and is promising to provide high-accuracy estimation of cortical source extents.
在本研究中，我们开发了一种新颖的电磁源成像方法，通过皮质电流密度（CCD）建模和一种新颖的脑电图成像算法来重建扩展的皮质源，该算法通过在目标函数中使用L1范数来探索皮质源表示中的稀疏性。新的稀疏皮质电流密度（SCCD）成像算法独具特色，因为它通过在变换域（皮质源分布的变化图）中实现稀疏性来重建皮质源。虽然预计在活跃和非活跃皮质区域之间的边界（稀疏性）会出现较大变化，但通过定位这些边界可以重建皮质源并估计其空间范围。我们使用大量模拟研究了SCCD算法，以研究其在重建不同范围的皮质源以及重建具有不同范围对比度的多个皮质源方面的能力。将SCCD算法与两种L2范数解决方案进行了比较，即加权最小范数估计（wMNE）和皮质LORETA。我们比较研究的模拟数据表明，所提出的稀疏源成像算法能够准确、高效地恢复扩展的皮质源，并有望提供皮质源范围的高精度估计。
