IEEE Trans Neural Syst Rehabil Eng. 2021;29:650-661. doi: 10.1109/TNSRE.2021.3064665. Epub 2021 Mar 16.
The electroencephalograph (EEG) source imaging (ESI) method is a non-invasive method that provides high temporal resolution imaging of brain electrical activity on the cortex. However, because the accuracy of EEG source imaging is often affected by unwanted signals such as noise or other source-irrelevant signals, the results of ESI are often incongruous with the real sources of brain activities. This study presents a novel ESI method (WPESI) that is based on wavelet packet transform (WPT) and subspace component selection to image the cerebral activities of EEG signals on the cortex. First, the original EEG signals are decomposed into several subspace components by WPT. Second, the subspaces associated with brain sources are selected and the relevant signals are reconstructed by WPT. Finally, the current density distribution in the cerebral cortex is obtained by establishing a boundary element model (BEM) from head MRI and applying the appropriate inverse calculation. In this study, the localization results obtained by this proposed approach were better than those of the original sLORETA approach (OESI) in the computer simulations and visual evoked potential (VEP) experiments. For epilepsy patients, the activity sources estimated by this proposed algorithm conformed to the seizure onset zones. The WPESI approach is easy to implement achieved favorable accuracy in terms of EEG source imaging. This demonstrates the potential for use of the WPESI algorithm to localize epileptogenic foci from scalp EEG signals.
脑电图（EEG）源成像（ESI）方法是一种非侵入性方法，可在皮层上提供大脑电活动的高时间分辨率成像。然而，由于 EEG 源成像的准确性通常受到噪声或其他与源无关的信号等不需要的信号的影响，因此 ESI 的结果通常与大脑活动的真实来源不一致。本研究提出了一种新的 ESI 方法（WPESI），该方法基于小波包变换（WPT）和子空间分量选择来对皮层上的 EEG 信号的大脑活动进行成像。首先，通过 WPT 将原始 EEG 信号分解为几个子空间分量。其次，选择与大脑源相关的子空间，并通过 WPT 重建相关信号。最后，通过对头 MRI 建立边界元模型（BEM）并应用适当的逆计算，获得大脑皮层中的电流密度分布。在本研究中，该方法在计算机模拟和视觉诱发电位（VEP）实验中的定位结果优于原始 sLORETA 方法（OESI）。对于癫痫患者，该算法估计的活动源与发作起始区一致。WPESI 方法易于实现，在 EEG 源成像方面具有良好的准确性。这表明 WPESI 算法有可能从头皮 EEG 信号定位致痫灶。
