EEG extended source localization: Tensor-based vs. conventional methods
EEG extended source localization: Tensor-based vs. conventional methods
复制标题
DOI：
10.1016/j.neuroimage.2014.03.043
复制
发表时间：
2014-08
期刊：
影响因子：
5.7
通讯作者：
H. Becker;L. Albera;P. Comon;M. Haardt;G. Birot;F. Wendling;M. Gavaret;C. Bénar;I. Merlet
中科院分区：
文献类型：
--
作者：
H. Becker;L. Albera;P. Comon;M. Haardt;G. Birot;F. Wendling;M. Gavaret;C. Bénar;I. Merlet
基于脑电图测量的脑源定位是过去几十年来引起广泛关注的一个话题，并且已经提出了许多不同的源定位算法。然而，在多个大脑区域同时活动且信噪比较低的情况下，它们的性能受到限制。为了克服这些问题，可以应用基于张量的预处理，其中包括构造时空频率（STF）或时空波矢量（STWV）张量并使用正则多元（CP）分解对其进行分解。在本文中，我们提出了一种基于张量分解结果的扩展源精确定位的新算法。此外，我们对基于张量的预处理方法进行了详细研究，包括分析其理论基础、计算复杂性以及与传统源定位算法（如 sLORETA、皮质 LORETA (cLORETA) 和 4-ExSo-MUSIC）相比对实际模拟数据的性能。我们的目标一方面是展示通过基于张量的预处理可以实现的性能增益，另一方面指出该方法的局限性和缺点。最后，我们在实际测量中验证了 STF 和 STWV 技术，以证明它们在实际应用中的有用性。
The localization of brain sources based on EEG measurements is a topic that has attracted a lot of attention in the last decades and many different source localization algorithms have been proposed. However, their performance is limited in the case of several simultaneously active brain regions and low signal-to-noise ratios. To overcome these problems, tensor-based preprocessing can be applied, which consists in constructing a space–time–frequency (STF) or space–time–wave–vector (STWV) tensor and decomposing it using the Canonical Polyadic (CP) decomposition. In this paper, we present a new algorithm for the accurate localization of extended sources based on the results of the tensor decomposition. Furthermore, we conduct a detailed study of the tensor-based preprocessing methods, including an analysis of their theoretical foundation, their computational complexity, and their performance for realistic simulated data in comparison to conventional source localization algorithms such as sLORETA, cortical LORETA (cLORETA), and 4-ExSo-MUSIC. Our objective consists, on the one hand, in demonstrating the gain in performance that can be achieved by tensor-based preprocessing, and, on the other hand, in pointing out the limits and drawbacks of this method. Finally, we validate the STF and STWV techniques on real measurements to demonstrate their usefulness for practical applications.