Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada.
Department of Electrical, Computer and Biomedical Engineering, Ryerson University, Toronto, Canada.
J Neural Eng. 2021 Feb 19;18(1). doi: 10.1088/1741-2552/abcc48.
Estimation of the source location within the brain from electroencephalography (EEG) and magnetoencephalography measures is a challenging task. Among the existing techniques in the field, which are known as brain imaging methods, standardized low-resolution brain electromagnetic tomography (sLORETA) is the most popular method due to its simplicity and high accuracy. However, in this work we illustrate that sLORETA is still noisy and the additive noise is causing the blurry image. The existing pre-fixed/manual thresholding process after sLORETA can partially take care of denoising. However, this ad-hoc theresholding can either remove so much of the desired data or leave much of the noise in the process. Manual correction to avoid such extreme cases can be time-consuming. The objective of this paper is to automate the denoising process in the form of adaptive thresholding.The proposed method, denoted by efficient high-resolution sLORETA (EHR-sLORETA), is based on minimizing the error between the desired denoised source and the source estimates.The approach is evaluated using synthetic EEG and real EEG data. spatial dispersion (SD), and mean square error (MSE) are used as metrics to provide the quantitative performance of the method. In addition, qualitative analysis of the method is provided for real EEG data. This proposed model demonstrates advantages over the existing methods in sense of accuracy and robustness with SD and MSE comparison.EHR-sLORETA could have a significant impact on clinical studies with source estimation task, as it improves the accuracy of source estimation and eliminates the need for manual thresholding.
从脑电图（EEG）和脑磁图（MEG）测量中估计脑内源位置是一项具有挑战性的任务。在该领域现有的技术中，标准化低分辨率脑电磁层析成像（sLORETA）因其简单性和高精度而成为最受欢迎的方法。然而，在这项工作中，我们说明了 sLORETA 仍然存在噪声，并且附加噪声导致图像模糊。sLORETA 之后的现有固定/手动阈值处理过程可以部分解决去噪问题。然而，这种特殊的阈值处理可能会去除太多所需的数据，或者在处理过程中留下太多噪声。为了避免这种极端情况，手动校正可能会很耗时。本文的目的是以自适应阈值处理的形式实现去噪过程的自动化。所提出的方法，记为高效高分辨率 sLORETA（EHR-sLORETA），基于最小化期望去噪源和源估计之间的误差。该方法使用合成 EEG 和真实 EEG 数据进行评估。空间分布（SD）和均方误差（MSE）用作指标，提供方法的定量性能。此外，还提供了真实 EEG 数据的定性分析。与现有方法相比，该方法在准确性和稳健性方面具有优势，通过 SD 和 MSE 比较可以看出。EHR-sLORETA 对具有源估计任务的临床研究可能具有重大影响，因为它提高了源估计的准确性并消除了手动阈值处理的需要。
