Department of Electrical Engineering, Indian Institute of Technology - Delhi, New Delhi, India.
Department of Electrical Engineering and Bharti School of Telecommunication, Indian Institute of Technology - Delhi, New Delhi, India.
Sci Rep. 2022 Jul 4;12(1):11240. doi: 10.1038/s41598-022-14500-7.
Brain Source Localization (BSL) using Electroencephalogram (EEG) has been a useful noninvasive modality for the diagnosis of epileptogenic zones, study of evoked related potentials, and brain disorders. The inverse solution of BSL is limited by high computational cost and localization error. The performance is additionally limited by head shape assumption and the corresponding harmonics basis function. In this work, an anatomical harmonics basis (Spherical Harmonics (SH), and more particularly Head Harmonics (H)) based BSL is presented. The spatio-temporal four shell head model is formulated in SH and H domain. The anatomical harmonics domain formulation leads to dimensionality reduction and increased contribution of source eigenvalues, resulting in decreased computation and increased accuracy respectively. The performance of spatial subspace based Multiple Signal Classification (MUSIC) and Recursively Applied and Projected (RAP)-MUSIC method is compared with the proposed SH and H counterparts on simulated data. SH and H domain processing effectively resolves the problem of high computational cost without sacrificing the inverse source localization accuracy. The proposed H MUSIC was additionally validated for epileptogenic zone localization on clinical EEG data. The proposed framework offers an effective solution to clinicians in automated and time efficient seizure localization.
脑源定位(BSL)使用脑电图(EEG)一直是诊断致痫区、诱发相关电位研究和脑部疾病的一种有用的非侵入性方式。BSL 的逆解受到计算成本高和定位误差的限制。性能还受到头形假设和相应的调和基函数的限制。在这项工作中,提出了一种基于解剖调和基(球调和(SH),特别是头调和(H))的 BSL。时空四壳头模型在 SH 和 H 域中进行公式化。解剖调和域公式化导致维度降低和源特征值的贡献增加,分别导致计算减少和准确性增加。在模拟数据上,将基于空间子空间的多信号分类(MUSIC)和递归应用和投影(RAP)-MUSIC 方法的性能与提出的 SH 和 H 对应方法进行了比较。SH 和 H 域处理有效地解决了计算成本高的问题,而不会牺牲逆源定位精度。还针对临床 EEG 数据上的致痫区定位验证了 H-MUSIC 的有效性。该框架为临床医生提供了一种自动、高效的癫痫定位解决方案。