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.

中文翻译：

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通过小波包变换和子空间分量选择提高脑电信号源成像精度

脑电图（EEG）源成像（ESI）方法是一种非侵入性方法，可提供大脑皮质电活动的高时间分辨率成像。但是，由于脑电图源成像的准确性通常受到诸如噪声或其他与源无关的信号之类的有害信号的影响，因此ESI的结果通常与大脑活动的真实来源不一致。这项研究提出了一种新颖的ESI方法（WPESI），该方法基于小波包变换（WPT）和子空间成分选择，以将EEG信号在大脑皮层上的大脑活动成像。首先，原始的EEG信号被WPT分解为几个子空间分量。其次，选择与脑源相关的子空间，并通过WPT重构相关信号。最后，通过从头部MRI建立边界元模型（BEM）并应用适当的逆计算，可获得大脑皮质中的当前密度分布。在这项研究中，通过这种拟议方法获得的定位结果在计算机仿真和视觉诱发电位（VEP）实验中要优于原始sLORETA方法（OESI）。对于癫痫患者，通过该提出的算法估计的活动来源符合癫痫发作区。WPESI方法很容易实现在脑电图源成像方面获得的良好精度。这证明了使用WPESI算法从头皮EEG信号中定位致癫痫灶的潜力。在这项研究中，通过这种拟议方法获得的定位结果在计算机仿真和视觉诱发电位（VEP）实验中要优于原始sLORETA方法（OESI）。对于癫痫患者，通过该提出的算法估计的活动来源符合癫痫发作区。WPESI方法很容易实现在脑电图源成像方面获得的良好精度。这证明了使用WPESI算法从头皮EEG信号中定位致癫痫灶的潜力。在这项研究中，通过这种拟议方法获得的定位结果在计算机仿真和视觉诱发电位（VEP）实验中要优于原始sLORETA方法（OESI）。对于癫痫患者，通过该提出的算法估计的活动来源符合癫痫发作区。WPESI方法很容易实现在脑电图源成像方面获得的良好精度。这证明了使用WPESI算法从头皮EEG信号中定位致癫痫灶的潜力。该算法估计的活动源符合癫痫发作区。WPESI方法很容易实现在脑电图源成像方面获得的良好精度。这证明了使用WPESI算法从头皮EEG信号中定位致癫痫灶的潜力。该算法估计的活动源符合癫痫发作区。WPESI方法很容易实现在脑电图源成像方面获得的良好精度。这证明了使用WPESI算法从头皮EEG信号中定位致癫痫灶的潜力。