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To study the accuracy of automated interictal EEG source localisation based on high-density EEG, and to compare it to low-density EEG. Methods: Thirty patients operated for pharmacoresistant focal epilepsy were retrospectively examined. Twelve months after resective brain surgery, 18 were seizure-free or had 'auras' only, while 12 had persistence of disabling seizures. Presurgical 257-channel EEG lasting 3-20 h was down-sampled to 25, 40, and 204 channels for separate analyses. For each electrode setup, interictal spikes were detected, clustered, and averaged automatically before validation by an expert reviewer. An individual 6-layer finite difference head model and the standardised low-resolution electromagnetic tomography were used to localise the maximum source activity of the most prevalent spike. Sublobar concordance with the resected brain area was visually assessed and related to favourable vs. unfavourable postsurgical outcome. Results: Depending on the EEG setup, epileptic spikes were detected in 21-24 patients (70-80%). The median number of single spikes per average was 470 (range 17-15,066). Diagnostic sensitivity of EEG source localisation was 58-75%, specificity was 50-67%, and overall accuracy was 55-71%. There were no significant differences between low-and high-density EEG setups with 25 to 257 electrodes. Conclusion: Automated high-density EEG source localisation provides meaningful information in the majority of cases. With hundreds of single spikes averaged, diagnostic accuracy is similar in high-and low-density EEG. Therefore, low-density EEG may be sufficient for interictal EEG source localisation if high numbers of spikes are available.

Objective Electric source imaging (ESI) of interictal epileptiform discharges (IEDs) has shown significant yield in numerous studies; however, its implementation at most centers is labor‐ and cost‐intensive. Semiautomatic ESI analysis (SAEA) has been proposed as an alternative and has previously shown benefit. Computer‐assisted automatic spike cluster retrieval, averaging, and source localization are carried out for each cluster and are then reviewed by an expert neurophysiologist, to determine their relevance for the individual case. Here, we examine its yield in a prospective single center study. Method Between 2017 and 2022, 122 patients underwent SAEA. Inclusion criteria for the current study were unifocal epilepsy disorder, epilepsy surgery with curative purpose, and postoperative follow‐up of 2 years or more. All patients (N=40) had continuous video‐electroencephalographic (EEG) monitoring with 37 scalp electrodes, which underwent SAEA. Forty patients matched our inclusion criteria. Results Twenty patients required intracranial monitoring; 13 were magnetic resonance imaging (MRI)‐negative. Mean duration of analyzed EEG was 4.3 days (±3.1 days), containing a mean of 12 749 detected IEDs (±22 324). The sensitivity, specificity, and accuracy of SAEA for localizing the epileptogenic focus of the entire group were 74.3%, 80%, and 75%, respectively, leading to an odds ratio (OR) of 11.5 to become seizure‐free if the source was included in the resection volume (p < .05). In patients with extratemporal lobe epilepsy, our results indicated an accuracy of 68% (OR=11.7). For MRI‐negative patients (n = 13) and patients requiring intracranial EEG (n = 20), we found a similarly high accuracy of 84.6% (OR=19) and 75% (OR = 15.9), respectively. Significance In this prospective study of SAEA of long‐term video‐EEG, spanning several days, we found excellent localizing information and a high yield, even in difficult patient groups. This compares favorably to high‐density ESI, most likely due to marked improved signal‐to‐noise ratio of the averaged IEDs. We propose including ESI, or SAEA, in the workup of all patients who are referred for epilepsy surgery.

To evaluate the yield of Functional Connectivity (FC) in addition to low-density ictal Electrical Source Imaging (ESI) in extratemporal lobe epilepsy (ETLE), using an automated algorithm for analysis. Method: Long-term EEG monitoring of consecutive ETLE patients who underwent surgery was reviewed by epileptologists, and seizure onsets characterized by rhythmical activity were identified. A spectrogram-based algorithm was developed to select objectively the parameters of ESI analysis. Two methods for SOZ localization were compared: i) ESI power, based on LORETA exclusively; ii) ESI + FC, including a Granger causality-based connectivity analysis. Results were determined at a sublobar level. The resection zone, in relation to 1-year follow-up surgical outcome, was considered as reference standard for diagnostic accuracy analyses. Results: Ninety-four seizures from 24 patients were analyzed. At seizure-level, ESI power showed 36 % sensitivity and 72 % specificity (accuracy: 45 %). ESI + FC significantly improved the accuracy, with 52 % sensitivity and 84 % specificity (accuracy: 61 %, p = 0.04). Results of ESI + FC were equally valuable in patients with lateralized or bilateral/generalized visual interpretation of ictal EEG. In a patient level sub-analysis, upon blinded clinical interpretation, ESI + FC showed a correct localization in 67 % of patients and substantial interrater agreement (kappa = 0.64), against 27 % achieved by ESI power, with fair inter-rater agreement (kappa = 0.37). Conclusion: FC significantly improves SOZ localization compared to ESI solely in ETLE. Ictal ESI + FC could represent a novel option in the armamentarium of presurgical evaluation, aiding also in patients with visually non-localizable scalp ictal EEG. Prospective studies evaluating the clinical added value of automated low-density ictal ESI may be justified.

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Objectives: High counts of averaged interictal epileptiform discharges (IEDs) are key components of accurate interictal electric source imaging (ESI) in patients with focal epilepsy. Automated detections may be time-efficient, but they need to identify the correct IED types. Thus we compared semiautomated and automated detection of IED types in long-term video-EEG (electroencephalography) monitoring (LTM) using an extended scalp EEG array and short-term highdensity EEG (hdEEG) with visual detection of IED types and the seizure-onset zone (SOZ). Methods:We prospectively recruited consecutive patients from four epilepsy centers who underwent both LTM with 40-electrode scalp EEG and short-term hdEEG with 256 electrodes. Only patients with a single circumscribed SOZ in LTM were included. In LTM and hdEEG, IED types were identified visually, semiautomatically and automatically. Concordances of semiautomated and automated detections in LTM and hdEEG, as well as visual detections in hdEEG, were compared against visually detected IED types and the SOZ in LTM.Results: Fifty-two of 62 patients with LTM and hdEEG were included. The most frequent IED types per patient, detected semiautomatically and automatically in LTM and visually in hdEEG, were significantly concordant with the most frequently visually identified IED type in LTM and the SOZ. Semiautomated and automated detections of IED types in hdEEG were significantly concordant with visually identified IED types in LTM, only when IED types with more than 50 detected single IEDs were selected. The threshold of 50 detected IED in hdEEG was reached in half of the patients. For all IED types per patient, agreement between visual and semiautomated detections in LTM was high.

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