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Interictal epileptiform discharges (IEDs) often co-occur across spatially-separated cortical regions, forming IED networks. However, the factors prompting IED propagation remain unelucidated. We hypothesized that slow oscillations (SOs) might facilitate IED propagation. Here, the amplitude and phase synchronization of SOs preceding propagating and non-propagating IEDs were compared in 22 patients with focal epilepsy undergoing intracranial electroencephalography (EEG) evaluation. Intracranial channels were categorized into the irritative zone (IZ) and normal zone (NOZ) regarding the presence of IEDs. During wakefulness, we found that pre-IED SOs within the IZ exhibited higher amplitudes for propagating IEDs than non-propagating IEDs (delta band: p = 0.001, theta band: p < 0.001). This increase in SOs was also concurrently observed in the NOZ (delta band: p = 0.04). Similarly, the inter-channel phase synchronization of SOs prior to propagating IEDs was higher than those preceding non-propagating IEDs in the IZ (delta band: p = 0.04). Through sliding window analysis, we observed that SOs preceding propagating IEDs progressively increased in amplitude and phase synchronization, while those preceding non-propagating IEDs remained relatively stable. Significant differences in amplitude occurred approximately 1150 ms before IEDs. During non-rapid eye movement (NREM) sleep, SOs on scalp recordings also showed higher amplitudes before intracranial propagating IEDs than before non-propagating IEDs (delta band: p = 0.006). Furthermore, the analysis of IED density around sleep SOs revealed that only high-amplitude sleep SOs demonstrated correlation with IED propagation. Overall, our study highlights that transient but widely distributed SOs are associated with IED propagation as well as generation in focal epilepsy during sleep and wakefulness, providing new insight into the EEG substrate supporting IED networks., Competing Interests: Declaration of Competing Interest The authors declared no conflict of interest. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: This study first aimed to establish the prevalence and predictors of subclinical seizures in patients with epilepsy undergoing video electroencephalographic monitoring, then to evaluate the relationship of sleep/wake and circadian pattern with subclinical seizures., Methods: We retrospectively reviewed the charts of 742 consecutive patients admitted to our epilepsy center between July 2012 and October 2014. Demographic, electro-clinical data and neuroimage were collected., Results: A total of 148 subclinical seizures were detected in 39 patients (5.3%) during video electroencephalographic monitoring. The mean duration of subclinical seizures was 47.18 s (range, 5-311). Pharmacoresistant epilepsy, abnormal MRI and the presence of interictal epileptiform discharges were independently associated with subclinical seizures in multivariate logistic regression analysis. Subclinical seizures helped localizing the presumed epileptogenic zone in 24 (61.5%) patients, and suggested multifocal epilepsy in five (12.8%). In addition, subclinical seizures occurred more frequently in sleep and night than wakefulness and daytime, respectively, and they were more likely seen between 21:00-03:00 h, and less likely seen between 09:00-12:00 h. Thirty patients (76.9%) had their first subclinical seizures within the first 24 h of monitoring while only 7.7% of patients had their first subclinical seizures detected within 20 min., Conclusion: Subclinical seizures are not uncommon in patients with epilepsy, particularly in those with pharmacoresistant epilepsy, abnormal MRI or interictal epileptiform discharges. Subclinical seizures occur in specific circadian patterns and in specific sleep/wake distributions. A 20-min VEEG monitoring might not be long enough to allow for their detection.

Objective: Many serious concerns exist in the long-term stability of brain-machine interfaces (BMIs) based on spike signals (single unit activity, SUA; multi unit activity, MUA). Some studies showed local field potentials (LFPs) could offer a stable decoding performance. However, the decoding stability of LFPs was examined only when high quality spike signals were recorded. Here we aim to examine the long-term decoding stability of LFPs over a larger time scale when the quality of spike signals was from good to poor or even no spike was recorded., Approach: Neural signals were collected from motor cortex of three monkeys via silicon arrays over 230, 290 and 690 days post-implantation when they performed 2D center out task. To compare long-term stability between LFPs and spike signals, we examined them in neural signals characteristics, directional tuning properties and offline decoding performance, respectively., Main Results: We observed slow decreasing trends in the number of LFP channels recorded and mean LFP power in different frequency bands when spike signals quality decayed over time. The number of significantly directional tuning LFP channels decreased more slowly than that of tuning SUA and MUA. The variable preferred directions for the same signal features across sessions indicated non-stationarity of neural activity. We also found that LFPs achieved better decoding performance than SUA and MUA in retrained decoder when the quality of spike signals seriously decayed. Especially, when no spike was recorded in one monkey after 671 days post-implantation, LFPs still provided some kinematic information. In addition, LFPs outperformed MUA in long-term decoding stability in a static decoder., Significance: Our results suggested that LFPs were more durable and could provide better decoding performance when spike signals quality seriously decayed. It might be due to their resistance to recording degradation and their high redundancy among channels.

Video-electroencephalographic monitoring (VEEG) is useful in the diagnosis of seizure disorders; however, its diagnostic yield in developing countries is not well known. The current study retrospectively reviewed the charts of 484 consecutive patients who were admitted to our center between July 2012 and September 2013. Of these patients, 298 (61.6%) were admitted for diagnostic clarification and underwent VEEG for a mean duration of 1.3days (range=1-9days). The patients were divided into two groups: those whose diagnosis was changed and those whose diagnosis was not changed as a result of VEEG. A patient with a preadmission diagnosis of epilepsy who was discharged with a diagnosis of nonepileptic events (NEEs) or who was further classified as focal/generalized epilepsy on discharge was included in the "change in diagnosis" group. A patient admitted with an uncertain diagnosis and discharged with a diagnosis of NEEs or epilepsy (including focal epilepsy and generalized epilepsy) was also included in the "change in diagnosis" group. Video-electroencephalographic monitoring recorded typical ictal events (epileptic events or nonepileptic events) in 147 (49.3%) of the patients admitted for diagnostic clarification. In total, 181 (60.7%) patients had a change in diagnosis after VEEG. Among them, 103 (56.9%) patients had a preadmission diagnosis of epilepsy, which was further classified as focal epilepsy (88 patients) or generalized epilepsy (15 patients); the diagnosis of NEEs and epilepsy was clarified in 78 (43.1%) patients. The number of patients diagnosed with NEEs increased from 31 (10.4%) on admission to 88 (29.5%) on discharge. Among all the patients admitted for diagnostic clarification, therapeutic plans were changed for 104 (57.5%) patients. In 117 (39.3%) patients with no diagnostic change, VEEG evaluation provided confirmative diagnostic information in 47 (15.8%) patients and no additional diagnostic information in 70 (23.5%) patients. The study indicates that VEEG is useful in terms of clarifying seizure diagnoses and evaluating seizure frequency. In our cohort study, VEEG of a relatively short mean duration produced a comparable diagnostic yield as that reported in other studies., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Effective seizure detection from long-term EEG is highly important for seizure diagnosis. Existing methods usually design the feature and classifier individually, while little work has been done for the simultaneous optimization of the two parts. This work proposes a deep network to jointly learn a feature and a classifier so that they could help each other to make the whole system optimal. To deal with the challenge of the impulsive noises and outliers caused by EMG artifacts in EEG signals, we formulate a robust stacked autoencoder (R-SAE) as a part of the network to learn an effective feature. In R-SAE, the maximum correntropy criterion (MCC) is proposed to reduce the effect of noise/outliers. Unlike the mean square error (MSE), the output of the new kernel MCC increases more slowly than that of MSE when the input goes away from the center. Thus, the effect of those noises/outliers positioned far away from the center can be suppressed. The proposed method is evaluated on six patients of 33.6 hours of scalp EEG data. Our method achieves a sensitivity of 100% and a specificity of 99%, which is promising for clinical applications.

Automatic seizure detection from the electroen-cephalogram (EEG) plays an important role in an on-demand closed-loop therapeutic system. A new feature, called IMF-VoE, is proposed to predict the occurrence of seizures. The IMF-VoE feature combines three intrinsic mode functions (IMFs) from the empirical mode decomposition of a EEG signal and the variance of the range between the upper and lower envelopes (VoE) of the signal. These multiple cues encode the intrinsic characteristics of seizure states, thus are able to distinguish them from the background. The feature is tested on 80.4 hours of EEG data with 10 seizures of 4 patients. The sensitivity of 100% is obtained with a low false detection rate of 0.16 per hour. Average time delays are 19.4s, 13.2s, and 10.7s at the false detection rates of 0.16 per hour, 0.27 per hour, and 0.41 per hour respectively, when different thresholds are used. The result is competitive among recent studies. In addition, since the IMF-VoE is compact, the detection system is of high computational efficiency and able to run in real time.

In this paper, a spike detection method is introduced. Traditional morphological filter is improved for extracting spikes from epileptic EEG signals and two key problems are addressed: morphological operation design and structure elements optimization. An average weighted combination of open-closing and close-opening operation, which can eliminate statistical deflection of amplitude, is utilized to separate background EEG and spikes. Then, according to the characteristic of spike component, the structure elements are constructed with two parabolas and a new criterion is put forward to optimize the structure elements. The proposed method is evaluated using normal and epileptic EEG data recorded from 12 test subjects. A comparison between the improved morphological filter, traditional morphological filter and wavelet analysis with Mexican hat function is presented, which indicates that the improved morphological filter is superior in restraining background activities. We demonstrate that the average detection rate of the improved morphological filter is much higher than that of the other two methods, and there is no false detection for normal EEG signals with the proposed method.

Electrocorticography (ECoG) has been demonstrated as a promising neural signal source for developing brain-machine interfaces (BMIs). However, many concerns about the disadvantages brought by large craniotomy for implanting the ECoG grid limit the clinical translation of ECoG-based BMIs. In this study, we collected clinical ECoG signals from the sensorimotor cortex of three epileptic participants when they performed hand gestures. The ECoG power spectrum in hybrid frequency bands was extracted to build a synchronous real-time BMI system. High decoding accuracy of the three gestures was achieved in both offline analysis (85.7%, 84.5%, and 69.7%) and online tests (80% and 82%, tested on two participants only). We found that the decoding performance was maintained even with a subset of channels selected by a greedy algorithm. More importantly, these selected channels were mostly distributed along the central sulcus and clustered in the area of 3 interelectrode squares. Our findings of the reduced and clustered distribution of ECoG channels further supported the feasibility of clinically implementing the ECoG-based BMI system for the control of hand gestures. [ABSTRACT FROM AUTHOR]

This article proposes a state-space model with Cauchy observation noise (SSMC) to detect seizure onset in a long-term EEG monitoring system. Facing the challenge of high false detection rates (FDRs) in many existing methods caused by impulsive EOG/EMG artifacts, the SSMC uses a nonlinear state-space model to encode the gradual changes of epileptic seizure signals and reject abrupt changes brought by artifacts. The Cauchy distribution is proposed to model impulsive observation noises because this heavy-tailed distribution is better at capturing abrupt changes in noise than Gaussian, thus reducing false alarms. Experiments are carried out on a dataset collected from an EEG-monitoring brain-machine interface system that contains 10 patients and 367 hours of EEG data. The authors' method achieves a high sensitivity of 100 percent with a low FDR of 0.08 per hour and a median time delay of 8.10 seconds, demonstrating the method's effectiveness. [ABSTRACT FROM PUBLISHER]