Article · Journal of Clinical Neurology · January 1, 2015
EEG Source Imaging in Partial Epilepsy in Comparison with Presurgical Evaluation and Magnetoencephalography
Chae Jung Park, Ji Hye Seo, Daeyoung Kim, Berdakh Abibullaev, Hyukchan Kwon, Yong‐Ho Lee
Abstract
BACKGROUND AND PURPOSE: The aim of this study was to determine the usefulness of three-dimensional (3D) scalp EEG source imaging (ESI) in partial epilepsy in comparison with the results of presurgical evaluation, magnetoencephalography (MEG), and electrocorticography (ECoG). METHODS: The epilepsy syndrome of 27 partial epilepsy patients was determined by presurgical evaluations. EEG recordings were made using 70 scalp electrodes, and the 3D coordinates of the electrodes were digitized. ESI images of individual and averaged spikes were analyzed by Curry software with a boundary element method. MEG and ECoG were performed in 23 and 9 patients, respectively. RESULTS: ESI and MEG source imaging (MSI) results were well concordant with the results of presurgical evaluations (in 96.3% and 100% cases for ESI and MSI, respectively) at the lobar level. However, there were no spikes in the MEG recordings of three patients. The ESI results were well concordant with MSI results in 90.0% of cases. Compared to ECoG, the ESI results tended to be localized deeper than the cortex, whereas the MSI results were generally localized on the cortical surface. ESI was well concordant with ECoG in 8 of 9 (88.9%) cases, and MSI was also well concordant with ECoG in 4 of 5 (80.0%) cases. The EEG single dipoles in one patient with mesial temporal lobe epilepsy were tightly clustered with the averaged dipole when a 3 Hz high-pass filter was used. CONCLUSIONS: The ESI results were well concordant with the results of the presurgical evaluation, MSI, and ECoG. The ESI analysis was found to be useful for localizing the seizure focus and is recommended for the presurgical evaluation of intractable epilepsy patients.
More by Berdakh Abibullaev
- Onset Review: traceable analysis of high-frequency oscillations and seizure onset in intracranial EEGZenodo · 2026
- Multi-scale EEG feature decoding with Swin Transformers for subject independent motor imagery BCIsScientific Reports · 2026 · Citations: 12
- Generative data augmentation for machine learning-based quantitative cancer cell detection using optical fiber biosensorsEPJ Web of Conferences · 2026
- Deep learning in intracranial EEG for seizure detection: advances, challenges, and clinical applicationsFrontiers in Neuroscience · 2025 · Citations: 13
