ABSTRACT
Epilepsy surgery is an important treatment modality for medically refractory focal epilepsy. The outcome of surgery usually depends on the localization accuracy of the epileptogenic zone (EZ) during pre-surgical evaluation. Good localization can be achieved with various electrophysiological and neuroimaging approaches. However, each approach has its own merits and limitations. Electroencephalography (EEG) Source Imaging (ESI) is an emerging model-based computational technique to localize cortical sources of electrical activity within the brain volume, three-dimensionally. ESI based pre-surgical evaluation gives an overall clinical yield of 73-91%, depending on choice of head model, inverse solution and EEG electrode density. It is a cost effective, non-invasive method which provides valuable additional information in presurgical evaluation due to its high localizing value specifically in MRI-negative cases, extra or basal temporal lobe epilepsy, multifocal lesions such as tuberous sclerosis or cases with multiple hypotheses. Unfortunately, less than 1% of surgical centers in developing countries use this method as a part of pre-surgical evaluation. This review promotes ESI as a useful clinical tool especially for patients with lesion-negative MRI to determine EZ cost-effectively with high accuracy under the optimized conditions.
ABSTRACT
EEG source localization is determining possible cortical sources of brain activities with scalp EEG. Generally, every step of the data processing sequence affects the accuracy of EEG source localization. In this paper, we introduce a fused multivariate empirical mode decomposing (MEMD) and inverse solution algorithm with an embedded unsupervised eye blink remover in order to localize the epileptogenic zone accurately. For this purpose, we constructed realistic forward models using MRI and boundary element method (BEM) for each patient to obtain results that are more realistic. We also developed an unsupervised algorithm utilizing a wavelet method to remove eye blink artifacts. Additionally, we applied MEMD, which is one of the recent and suitable feature extraction methods for non-linear, non-stationary, and multivariate signals such as EEG, to extract the signal of interest. We examined the localization results using the two most reliable linear distributed inverse methods in the literature: weighted minimum norm estimation (wMN) and standardized low resolution tomography (sLORETA). Results affirm the success of the proposed algorithm with the highest agreement compared to MRI reference by a specialist. Fusion of MEMD and sLORETA results in approximately zero localization error in terms of spatial difference with the validated MRI reference. High accuracy results of proposed algorithm using non-invasive and low-resolution EEG provide the potential of using this work for pre-surgical evaluation towards epileptogenic zone localization in clinics.
