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OBJECTIVE: Infantile epileptic spasms (IS) are epileptic seizures that are associated with increased risk for developmental impairments, adult epilepsies, and mortality. Here, we investigated coherence-based network dynamics in scalp EEG of infants with IS to identify frequency-dependent networks associated with spasms. We hypothesized that there is a network of increased fast ripple connectivity during the electrographic onset of clinical spasms, which is distinct from controls. METHODS: We retrospectively analyzed peri-ictal and interictal EEG recordings of 14 IS patients. The data was compared with 9 age-matched controls. Wavelet phase coherence (WPC) was computed between 0.2 and 400 Hz. Frequency- and time-dependent brain networks were constructed using this coherence as the strength of connection between two EEG channels, based on graph theory principles. Connectivity was evaluated through global efficiency (GE) and channel-based closeness centrality (CC), over frequency and time. RESULTS: GE in the fast ripple band (251-400 Hz) was significantly greater following the onset of spasms in all patients (P  central > anterior, Kruskal-Wallis P

• Ictal eye movements and nystagmus could be signs of ongoing seizure activity in patients with cerebral hyperperfusion syndrome. • The frontal eye field and parietal eye field cause different ictal eye movements with epileptic nystagmus. • Scalp EEG has an important role in defining the ictal origin of epileptic eye movements and nystagmus. [ABSTRACT FROM AUTHOR]

At present, at least 30% of refractory epilepsy patients in the world cannot be effectively controlled and treated. The suddenness and unpredictability of seizures greatly affect the physical and mental health and even the life safety of patients, and the realization of early prediction of seizures and the adoption of interventions are of great significance to the improvement of patients' quality of life. In this paper, we firstly introduce the design process of EEG-based seizure prediction methods, introduce several databases commonly used in the research, and summarize the commonly used methods in pre-processing, feature extraction, classification and identification, and post-processing. Then, based on scalp EEG and intracranial EEG respectively, we reviewed the current status of epileptic seizure prediction research from five commonly used feature analysis methods, and make a comprehensive evaluation of both. Finally, this paper describes the reasons why the current algorithms cannot be applied to the clinic, summarizes their limitations, and gives corresponding suggestions, aiming to provide improvement directions for subsequent research. In addition, deep learning algorithms have emerged in recent years, and this paper also compares the advantages and disadvantages of deep learning algorithms with traditional machine learning methods, in the hope of providing researchers with new technologies and new ideas and making significant breakthroughs in the field of epileptic seizure prediction. [ABSTRACT FROM AUTHOR]

Introduction: Wearable in-ear electroencephalographic (EEG) devices hold significant promise for integrating brain monitoring technologies into real-life applications. However, despite the introduction of various in-ear EEG systems, there remains a necessity for validating these technologies against goldstandard, clinical-grade devices. This study aims to evaluate the signal quality of a newly developed mobile in-ear EEG device compared to a standard scalp EEG system among healthy volunteers during wakefulness and sleep. Methods: The study evaluated an in-ear EEG device equipped with dry electrodes in a laboratory setting, recording a single bipolar EEG channel using a cross-ear electrode configuration. Thirty healthy participants were recorded simultaneously using the in-ear EEG device and a conventional EEG cap system with 64 wet electrodes. Based on two recording protocols, one during a resting state condition involving alternating eye opening and closure with a low degree of artifact contamination and another consisting of a daytime nap, several quality measures were used for a quantitative comparison including root mean square (RMS) analysis, artifact quantification, similarities of relative spectral power (RSP), signal-to-noise ratio (SNR) based on alpha peak criteria, and cross-signal correlations of alpha activity during eyes-closed conditions and sleep activities. The statistical significance of our results was assessed through nonparametric permutation tests with False Discovery Rate (FDR) control. Results: During the resting state, in-ear and scalp EEG signals exhibited similar fluctuations, characterized by comparable RMS values. However, intermittent signal alterations were noticed in the in-ear recordings during nap sessions, attributed to movements of the head and facial muscles. Spectral analysis indicated similar patterns between in-ear and scalp EEG, showing prominent peaks in the alpha range (8-12 Hz) during rest and in the low-frequency range during naps (particularly in the theta range of 4-7 Hz). Analysis of alpha wave characteristics during eye closures revealed smaller alpha wave amplitudes and slightly lower signal-to-noise ratio (SNR) values in the in-ear EEG compared to scalp EEG. In around 80% of cases, cross-correlation analysis between in-ear and scalp signals, using a contralateral bipolar montage of 64 scalp electrodes, revealed significant correlations with scalp EEG (p < 0.01), particularly evident in the FT11-FT12 and T7-T8 electrode derivations. Conclusion: Our findings support the feasibility of using in-ear EEG devices with dry-contact electrodes for brain activity monitoring, compared to a standard scalp EEG, notably for wakefulness and sleep uses. Although marginal signal degradation is associated with head and facial muscle contractions, the in-ear device offers promising applications for long-term EEG recordings, particularly in scenarios requiring enhanced comfort and user-friendliness. [ABSTRACT FROM AUTHOR]

The Penn Electrophysiology of Encoding and Retrieval Study (PEERS) aimed to characterize the behavioral and electrophysiological (EEG) correlates of memory encoding and retrieval in highly practiced individuals. Across five PEERS experiments, 300+ subjects contributed more than 7,000 memory testing sessions with recorded EEG data. Here we tell the story of PEERS: its genesis, evolution, major findings, and the lessons it taught us about taking a big scientific approach in studying memory and the human brain. [ABSTRACT FROM AUTHOR]

Purpose: Coherence analysis in electroencephalography (EEG) allows measurement of the degree of consistency of amplitude between pairs of electrodes. Theoretically, disconnective epilepsy surgery should decrease coherence between corresponding areas. The study aimed to evaluate postoperative changes in interhemispheric coherence values after corpus callosotomy (CC). Methods: Non-lesional, drug-resistant, generalized epilepsy patients who underwent total CC were retrospectively collected. To evaluate coherence, we divided the scalp interictal EEG into "baseline" and "discharge" states after excluding periods with artifacts. Interhemispheric coherence values were obtained between eight pairs of symmetrically opposite scalp electrodes in six different frequency bands. We analyzed both pre- and postoperative EEG sessions and calculated the percentage of difference (POD) in coherence values. Results: We collected 13 patients and analyzed 2496 interhemispheric coherence values. Preoperative coherence values differed significantly between baseline and discharge states (p = 0.0003), but postoperative values did not (p = 0.11). For baseline state, coherence values were decreased after CC and median POD was − 22.3% (p < 0.0001). Delta frequency showed the most decreased POD (-44.3%, p = 0.0009). Median POD was lowest in the Fp1-Fp2 pair of electrodes. For discharge state, coherence values were decreased after CC and median POD was − 24.7% (p < 0.0001). Delta frequency again showed the most decreased POD (-55.9%, p = 0.0016). Median POD was lowest in the F7-F8 pair. Conclusion: After total CC, interhemispheric coherence decreased significantly in both baseline and discharge states. The most decreased frequency band was the delta band, which may be used as a representative frequency band in future studies. [ABSTRACT FROM AUTHOR]

• Early detection of the onset of childhood absence seizures is mandatory to deliver an external stimulation able to inhibit them. • An on-line process is developed to detect the onset of absence seizures from four scalp EEG electrodes. • The proposed unsupervised solution detects most seizure onsets within a very short delay of 200 or 500 ms. This study aims to detect the seizure onset, in childhood absence epilepsy, as early as possible. Indeed, interfering with absence seizures with sensory simulation has been shown to be possible on the condition that the stimulation occurs soon enough after the seizure onset. We present four variations (two supervised, two unsupervised) of an algorithm designed to detect the onset of absence seizures from 4 scalp electrodes, and compare their performance with that of a state-of-the-art algorithm. We exploit the characteristic shape of spike-wave discharges to detect the seizure onset. Their performance is assessed on clinical electroencephalograms from 63 patients with confirmed childhood absence epilepsy. The proposed approaches succeed in early detection of the seizure onset, contrary to the classical detection algorithm. Indeed, the results clearly show the superiority of the proposed methods for small delays of detection, under 750 ms from the onset. The performance of the proposed unsupervised methods is equivalent to that of the supervised ones. The use of only four electrodes makes the pipeline suitable to be embedded in a wearable device. The proposed pipelines perform early detection of absence seizures, which constitutes a prerequisite for a closed-loop system. [ABSTRACT FROM AUTHOR]

Topological data analysis (TDA) has proven to be a potent approach for extracting intricate topological structures from complex and high-dimensional data. In this paper, we propose a TDA-based processing pipeline for analyzing multi-channel scalp EEG data. The pipeline starts with extracting both frequency and temporal information from the signals via the Hilbert–Huang Transform. The sequences of instantaneous frequency and instantaneous amplitude across all electrode channels are treated as approximations of curves in the high-dimensional space. TDA features, which represent the local topological structure of the curves, are further extracted and used in the classification models. Three sets of scalp EEG data, including one collected in a lab and two Brain–computer Interface (BCI) competition data, were used to validate the proposed methods, and compare with other state-of-art TDA methods. The proposed TDA-based approach shows superior performance and outperform the winner of the BCI competition. Besides BCI, the proposed method can also be applied to spatial and temporal data in other domains such as computer vision, remote sensing, and medical imaging. [ABSTRACT FROM AUTHOR]

IntroductionWearable in-ear electroencephalographic (EEG) devices hold significant promise for integrating brain monitoring technologies into real-life applications. However, despite the introduction of various in-ear EEG systems, there remains a necessity for validating these technologies against gold-standard, clinical-grade devices. This study aims to evaluate the signal quality of a newly developed mobile in-ear EEG device compared to a standard scalp EEG system among healthy volunteers during wakefulness and sleep.MethodsThe study evaluated an in-ear EEG device equipped with dry electrodes in a laboratory setting, recording a single bipolar EEG channel using a cross-ear electrode configuration. Thirty healthy participants were recorded simultaneously using the in-ear EEG device and a conventional EEG cap system with 64 wet electrodes. Based on two recording protocols, one during a resting state condition involving alternating eye opening and closure with a low degree of artifact contamination and another consisting of a daytime nap, several quality measures were used for a quantitative comparison including root mean square (RMS) analysis, artifact quantification, similarities of relative spectral power (RSP), signal-to-noise ratio (SNR) based on alpha peak criteria, and cross-signal correlations of alpha activity during eyes-closed conditions and sleep activities. The statistical significance of our results was assessed through nonparametric permutation tests with False Discovery Rate (FDR) control.ResultsDuring the resting state, in-ear and scalp EEG signals exhibited similar fluctuations, characterized by comparable RMS values. However, intermittent signal alterations were noticed in the in-ear recordings during nap sessions, attributed to movements of the head and facial muscles. Spectral analysis indicated similar patterns between in-ear and scalp EEG, showing prominent peaks in the alpha range (8–12 Hz) during rest and in the low-frequency range during naps (particularly in the theta range of 4–7 Hz). Analysis of alpha wave characteristics during eye closures revealed smaller alpha wave amplitudes and slightly lower signal-to-noise ratio (SNR) values in the in-ear EEG compared to scalp EEG. In around 80% of cases, cross-correlation analysis between in-ear and scalp signals, using a contralateral bipolar montage of 64 scalp electrodes, revealed significant correlations with scalp EEG (p

Abstract Epilepsy surgery is effective for patients with medication-resistant seizures, however 20–40% of them are not seizure free after surgery. Aim of this study is to evaluate the role of linear and non-linear EEG features to predict post-surgical outcome. We included 123 paediatric patients who underwent epilepsy surgery at Bambino Gesù Children Hospital (January 2009–April 2020). All patients had long term video-EEG monitoring. We analysed 1-min scalp interictal EEG (wakefulness and sleep) and extracted 13 linear and non-linear EEG features (power spectral density (PSD), Hjorth, approximate entropy, permutation entropy, Lyapunov and Hurst value). We used a logistic regression (LR) as feature selection process. To quantify the correlation between EEG features and surgical outcome we used an artificial neural network (ANN) model with 18 architectures. LR revealed a significant correlation between PSD of alpha band (sleep), Mobility index (sleep) and the Hurst value (sleep and awake) with outcome. The fifty-four ANN models gave a range of accuracy (46–65%) in predicting outcome. Within the fifty-four ANN models, we found a higher accuracy (64.8% ± 7.6%) in seizure outcome prediction, using features selected by LR. The combination of PSD of alpha band, mobility and the Hurst value positively correlate with good surgical outcome.

Epilepsy surgery is effective for patients with medication-resistant seizures, however 20–40% of them are not seizure free after surgery. Aim of this study is to evaluate the role of linear and non-linear EEG features to predict post-surgical outcome. We included 123 paediatric patients who underwent epilepsy surgery at Bambino Gesù Children Hospital (January 2009–April 2020). All patients had long term video-EEG monitoring. We analysed 1-min scalp interictal EEG (wakefulness and sleep) and extracted 13 linear and non-linear EEG features (power spectral density (PSD), Hjorth, approximate entropy, permutation entropy, Lyapunov and Hurst value). We used a logistic regression (LR) as feature selection process. To quantify the correlation between EEG features and surgical outcome we used an artificial neural network (ANN) model with 18 architectures. LR revealed a significant correlation between PSD of alpha band (sleep), Mobility index (sleep) and the Hurst value (sleep and awake) with outcome. The fifty-four ANN models gave a range of accuracy (46–65%) in predicting outcome. Within the fifty-four ANN models, we found a higher accuracy (64.8% ± 7.6%) in seizure outcome prediction, using features selected by LR. The combination of PSD of alpha band, mobility and the Hurst value positively correlate with good surgical outcome. [ABSTRACT FROM AUTHOR]

Sleep is assumed to be a unitary, global state in humans and most other animals that is coordinated by executive centers in the brain stem, hypothalamus, and basal forebrain. However, the common observation of unihemispheric sleep in birds and marine mammals, as well as the recently discovered nonpathological regional sleep in rodents, calls into question whether the whole human brain might also typically exhibit different states between brain areas at the same time. We analyzed sleep states independently from simultaneously recorded hippocampal depth electrodes and cortical scalp electrodes in eight human subjects who were implanted with depth electrodes for pharmacologically intractable epilepsy evaluation. We found that the neocortex and hippocampus could be in nonsimultaneous states, on average, one-third of the night and that the hippocampus often led in asynchronous state transitions. Nonsimultaneous bout lengths varied from 30 s to over 30 min. These results call into question the conclusions of studies, across phylogeny, that measure only surface cortical state but seek to assess the functions and drivers of sleep states throughout the brain.

Abstract Objective Infantile epileptic spasms (IS) are epileptic seizures that are associated with increased risk for developmental impairments, adult epilepsies, and mortality. Here, we investigated coherence‐based network dynamics in scalp EEG of infants with IS to identify frequency‐dependent networks associated with spasms. We hypothesized that there is a network of increased fast ripple connectivity during the electrographic onset of clinical spasms, which is distinct from controls. Methods We retrospectively analyzed peri‐ictal and interictal EEG recordings of 14 IS patients. The data was compared with 9 age‐matched controls. Wavelet phase coherence (WPC) was computed between 0.2 and 400 Hz. Frequency‐ and time‐dependent brain networks were constructed using this coherence as the strength of connection between two EEG channels, based on graph theory principles. Connectivity was evaluated through global efficiency (GE) and channel‐based closeness centrality (CC), over frequency and time. Results GE in the fast ripple band (251–400 Hz) was significantly greater following the onset of spasms in all patients (P central > anterior, Kruskal‐Wallis P

Abstract Objective We aimed to investigate (1) whether an automated detector can capture scalp high‐frequency oscillations (HFO) in neonates and (2) whether scalp HFO rates can differentiate neonates with seizures from healthy neonates. Methods We considered 20 neonates with EEG‐confirmed seizures and four healthy neonates. We applied a previously validated automated HFO detector to determine scalp HFO rates in quiet sleep. Results Etiology in neonates with seizures included hypoxic‐ischemic encephalopathy in 11 cases, structural vascular lesions in 6, and genetic causes in 3. The HFO rates were significantly higher in neonates with seizures (0.098 ± 0.091 HFO/min) than in healthy neonates (0.038 ± 0.025 HFO/min; P = 0.02) with a Hedge's g value of 0.68 indicating a medium effect size. The HFO rate of 0.1 HFO/min/ch yielded the highest Youden index in discriminating neonates with seizures from healthy neonates. In neonates with seizures, etiology, status epilepticus, EEG background activity, and seizure patterns did not significantly impact HFO rates. Significance Neonatal scalp HFO can be detected automatically and differentiate neonates with seizures from healthy neonates. Our observations have significant implications for neuromonitoring in neonates. This is the first step in establishing neonatal HFO as a biomarker for neonatal seizures.

• High-frequency oscillations (HFOs) in interictal scalp EEG in patients with Panayiotopoulos syndrome (PS) were analyzed. • The longer the duration of HFO appearance, the longer the duration of seizure activity. • Scalp-recorded HFOs are an important biomarker of epileptogenicity and seizure activity of PS. We studied the relationship between the clinical course of Panayiotopoulos syndrome (PS) and high-frequency oscillations (HFOs) captured during interictal scalp electroencephalography (EEG) to determine the feasibility of using HFOs to detect seizure activity in PS. We analyzed the interictal scalp EEGs of 18 children with PS. Age parameters, seizure frequencies, and antiepileptic drugs were compared between the HFO-positive (HFOPG) and HFO-negative (HFONG) groups. Thirteen patients (72.2%) had HFOs while five patients (27.8%) had no HFOs in 194 interictal EEG records. We found no statistically significant differences in the mean age of epilepsy onset and last seizure, seizure frequency, or frequency of status epilepticus. However, the seizure activity period of the HFOPG was significantly longer than that of the HFONG. Patients with an HFO duration longer than 2 years were intractable to treatment. In most cases, seizures did not occur in the absence of HFOs, even when the spikes remained. HFOs are related to the seizure activity period in patients with PS. We propose that HFOs are a biomarker of epileptogenicity and an indicator for drug reduction because seizures did not occur if HFOs disappeared even if the spikes remained. [ABSTRACT FROM AUTHOR]

Objective: We aimed to investigate (1) whether an automated detector can capture scalp high‐frequency oscillations (HFO) in neonates and (2) whether scalp HFO rates can differentiate neonates with seizures from healthy neonates. Methods: We considered 20 neonates with EEG‐confirmed seizures and four healthy neonates. We applied a previously validated automated HFO detector to determine scalp HFO rates in quiet sleep. Results: Etiology in neonates with seizures included hypoxic‐ischemic encephalopathy in 11 cases, structural vascular lesions in 6, and genetic causes in 3. The HFO rates were significantly higher in neonates with seizures (0.098 ± 0.091 HFO/min) than in healthy neonates (0.038 ± 0.025 HFO/min; P = 0.02) with a Hedge's g value of 0.68 indicating a medium effect size. The HFO rate of 0.1 HFO/min/ch yielded the highest Youden index in discriminating neonates with seizures from healthy neonates. In neonates with seizures, etiology, status epilepticus, EEG background activity, and seizure patterns did not significantly impact HFO rates. Significance: Neonatal scalp HFO can be detected automatically and differentiate neonates with seizures from healthy neonates. Our observations have significant implications for neuromonitoring in neonates. This is the first step in establishing neonatal HFO as a biomarker for neonatal seizures. [ABSTRACT FROM AUTHOR]

Topological data analysis (TDA) has proven to be a potent approach for extracting intricate topological structures from complex and high-dimensional data. In this paper, we propose a TDA-based processing pipeline for analyzing multi-channel scalp EEG data. The pipeline starts with extracting both frequency and temporal information from the signals via the Hilbert–Huang Transform. The sequences of instantaneous frequency and instantaneous amplitude across all electrode channels are treated as approximations of curves in the high-dimensional space. TDA features, which represent the local topological structure of the curves, are further extracted and used in the classification models. Three sets of scalp EEG data, including one collected in a lab and two Brain–computer Interface (BCI) competition data, were used to validate the proposed methods, and compare with other state-of-art TDA methods. The proposed TDA-based approach shows superior performance and outperform the winner of the BCI competition. Besides BCI, the proposed method can also be applied to spatial and temporal data in other domains such as computer vision, remote sensing, and medical imaging.

Objective: Although vagus nerve stimulation (VNS) is a common and widely used therapy for pharmacoresistant epilepsy, the reported efficacy of VNS in pediatric patients varies, so it is unclear which children will respond to VNS therapy. This study aimed to identify functional brain network features associated with VNS action to distinguish VNS responders from nonresponders using scalp electroencephalogram (EEG) data. Methods: Twenty‐three children were included in this study, 16 in the discovery cohort and 7 in the test cohort. Using partial correlation value as a measure of whole‐brain functional connectivity, we identified the differential edges between responders and nonresponders. Results derived from this were used as input to generate a support vector machine‐learning classifier to predict VNS outcomes. Results: The postcentral gyrus in the left and right parietal lobe regions was identified as the most significant differential brain region between VNS responders and nonresponders (p < 0.001). The resultant classifier demonstrated a mean AUC value of 0.88, a mean sensitivity rate of 91.4%, and a mean specificity rate of 84.3% on fivefold cross‐validation in the discovery cohort. In the testing cohort, our study demonstrated an AUC value of 0.91, a sensitivity rate of 86.6%, and a specificity rate of 79.3%. Furthermore, for prediction accuracy, our model can achieve 81.4% accuracy at the epoch level and 100% accuracy at the patient level. Significance: This study provides the first treatment response prediction model for VNS using scalp EEG data with ictal recordings and offers new insights into its mechanism of action. Our results suggest that brain functional connectivity features can help predict therapeutic response to VNS therapy. With further validation, our model could facilitate the selection of targeted pediatric patients and help avoid risky and costly procedures for patients who are unlikely to benefit from VNS therapy. [ABSTRACT FROM AUTHOR]

Abstract High‐frequency oscillations (HFO) in scalp EEG are a new and promising noninvasive epilepsy biomarker, providing added prognostic value, particularly in pediatric lesional epilepsy. However, it is unclear if lesion characteristics, such as lesion volume, depth, type, and localization, impact scalp HFO rates. We analyzed scalp EEG from 13 children and adolescents with focal epilepsy associated with focal cortical dysplasia (FCD), low‐grade tumors, or hippocampal sclerosis. We applied a validated automated detector to determine HFO rates in bipolar channels. We identified the lesion characteristics in MRI. Larger lesions defined by MRI volumetric analysis corresponded to higher cumulative scalp HFO rates (P = .01) that were detectable in a higher number of channels (P = .05). Both superficial and deep lesions generated HFO detectable in the scalp EEG. Lesion type (FCD vs tumor) and lobar localization (temporal vs extratemporal) did not affect scalp HFO rates in our study. Our observations support that all lesions may generate HFO detectable in scalp EEG, irrespective of their characteristics, whereas larger epileptogenic lesions generate higher scalp HFO rates over larger areas that are thus more accessible to detection. Our study provides crucial insight into scalp HFO detectability in pediatric lesional epilepsy, facilitating their implementation as an epilepsy biomarker in a clinical setting.

Abstract Objective Focal cortical dysplasia (FCD) represents an essential cause of drug‐resistant epilepsy with surgery as an effective treatment option. This study aimed to identify the important predictors of favorable surgical outcomes and the impact of the interictal scalp electroencephalogram (EEG) patterns in predicting postsurgical seizure outcomes. Methods We retrospectively evaluated 210 consecutive patients between 2015 and 2019. They were diagnosed with FCD by pathology, underwent resection, and had at least one year of postsurgical follow‐up. Predictors of seizure freedom were analyzed. Results Based on the information at the latest follow‐up, seizure outcome was classified as Engel Class I (seizure‐free) in 81.4% and Engel Class II‐IV (non‐seizure‐free) in 18.6% of patients. There were 43, 105, and 62 cases of FCD type I, type II, and type III, respectively. The interictal EEG showed a repetitive discharge pattern (REDP) in 87 (41.4%) patients, polyspike discharge pattern (PDP) in 41 (19.5%), and the coexistence of REDP and PDP in the same location in 32 (15.2%) patients. The analyzed patterns in order of frequency were repetitive discharges lasting 5 seconds or more (32.4%); polyspikes (16.7%); RED type 1 (11.4%); continuous epileptiform discharges occupying >80% of the recording (11.4%); RED type 2 (6.2%); brushes (3.3%); focal, fast, continuous spikes (2.4%); focal fast rhythmic epileptiform discharges (1.43%); and frequent rhythmic bursting epileptiform activity (1.4%). The coexistence of REDP and PDP in the same location on scalp EEG and complete resection of the assumed epileptogenic zone (EZ) was independently associated with favorable postsurgical prognosis. Significance Resective epilepsy surgery for intractable epilepsy caused by FCD has favorable outcomes. Interictal scalp EEG patterns were revealed to be predictive of excellent surgical outcomes and may help clinical decision‐making and enable better presurgical evaluation.

• Intracranial EEG may demonstrate regional postictal EEG suppression (PES) despite simultaneous scalp EEG showing either generalized PES or no PES. • The extent of intracranial PES is associated with severity of postictal autonomic dysfunction as measured by heart rate variability. • PES duration and extent are similar in non-fatal seizures between patients with and without sudden unexpected death in epilepsy (SUDEP). The association between postictal electroencephalogram (EEG) suppression (PES), autonomic dysfunction, and Sudden Unexpected Death in Epilepsy (SUDEP) remains poorly understood. We compared PES on simultaneous intracranial and scalp-EEG and evaluated the association of PES with postictal heart rate variability (HRV) and SUDEP outcome. Convulsive seizures were analyzed in patients with drug-resistant epilepsy at 5 centers. Intracranial PES was quantified using the Hilbert transform. HRV was quantified using root mean square of successive differences of interbeat intervals, low-frequency to high-frequency power ratio, and RR-intervals. There were 64 seizures from 63 patients without SUDEP and 11 seizures from 6 SUDEP patients. PES occurred in 99% and 87% of seizures on intracranial-EEG and scalp-EEG, respectively. Mean PES duration in intracranial and scalp-EEG was similar. Intracranial PES was regional (<90% of channels) in 46% of seizures; scalp PES was generalized in all seizures. Generalized PES showed greater decrease in postictal parasympathetic activity than regional PES. PES duration and extent were similar between patients with and without SUDEP. Regional intracranial PES can be present despite scalp-EEG demonstrating generalized or no PES. Postictal autonomic dysfunction correlates with the extent of PES. Intracranial-EEG demonstrates changes in autonomic regulatory networks not seen on scalp-EEG. [ABSTRACT FROM AUTHOR]

A growing collection of observations has demonstrated the presence of multiple neural oscillations participating in human temporal cognition and psychiatric pathologies such as depression and anxiety. However, there remains a gap in the literature regarding the specific roles of these neural oscillations during interval timing, and how these oscillatory activities might vary with the different levels of mental health. The current study examined the participation of the frontal midline theta and occipital alpha oscillations, both of which are prevalent cortical oscillatory markers frequently reported in working memory and time perception paradigms. Participants performed a time reproduction task in the sub- (400, 600, 800 ms) and supra-second timescales (1600, 1800, 2000 ms) while undergoing scalp EEG recordings. Anxiety and depression levels were measured via self-report mental health inventories. Time–frequency analysis of scalp EEG revealed that both frontal midline and occipital alpha oscillations were engaged during the encoding of the durations. Furthermore, we observed that the correlational relationship between frontal midline theta power and the reproduction performance in the sub-second range was modulated by state anxiety. In contrast, the correlational relationship between occipital alpha and the reproduction performance of supra-second intervals was modulated by depression and trait anxiety. The results offer insights on how alpha and theta oscillations differentially play a role in interval timing and how mental health further differentially relates these neural oscillations to sub- and supra-second timescales. [ABSTRACT FROM AUTHOR]

Visual inspection of long-term electroencephalography (EEG) is a tedious task for physicians in neurology. Based on bidirectional gated recurrent unit (Bi-GRU) neural network, an automatic seizure detection method is proposed in this paper to facilitate the diagnosis and treatment of epilepsy. Firstly, wavelet transforms are applied to EEG recordings for filtering pre-processing. Then the relative energies of signals in several particular frequency bands are calculated and inputted into Bi-GRU network. Afterwards, the outputs of Bi-GRU network are further processed by moving average filtering, threshold comparison and seizure merging to generate the discriminant results that the tested EEG belong to seizure or not. Evaluated on CHB-MIT scalp EEG database, the proposed seizure detection method obtained an average sensitivity of 93.89% and an average specificity of 98.49%. 124 out of 128 seizures were correctly detected and the achieved average false detection rate was 0.31 per hour on 867.14 h testing data. The results show the superiority of Bi-GRU network in seizure detection and the proposed detection method has a promising potential in the monitoring of long-term EEG.

Background: Understanding functional connectivity (FC) patterns of epileptic brain networks as they relate to the presence or absence of interictal epileptiform discharges (IEDs) can enhance machine learning (ML) algorithms identifying them. Methods: Changes in brain dynamics induced by the presence of IEDs are demonstrated by constructing FC maps from scalp electroencephalography (EEG) data. The intent is to demonstrate how unique IED characteristics present in the FC maps could be useful in training ML algorithms to yield an effective IED detection process. Results: a) The active frontal-temporal (FT) region as predetermined by the neurologists during an IED segment is found to be characterized by a statistically significant increase in the average local FC over the other FT region and over FT regions with the highest average local FC when using non-IED (NIED) segments of the same patient. This statistical significance is found for the theta, alpha, and beta sub-bands. b) Distant connections coupling one region to another also show a statistically significant difference between IED and NIED segments. Depending on the IED morphology, the significant sub-band matching those findings differs from patient to patient. Hence, while the theta sub-band results in the highest area under receiver operating characteristic curve (ROC-AUC) among the rest, it is still important to include features of other sub-bands since together they yield even higher ROC-AUC. Conclusions: FC maps intrinsically reflect the significant changes occurring in the dynamics of the epileptic brain. The obtained results provide added confidence in utilizing FC maps as biomarkers for detecting IEDs.

Sleep is assumed to be a unitary, global state in humans and most other animals that is coordinated by executive centers in the brain stem, hypothalamus, and basal forebrain. However, the common observation of unihemispheric sleep in birds and marine mammals, as well as the recently discovered nonpathological regional sleep in rodents, calls into question whether the whole human brain might also typically exhibit different states between brain areas at the same time. We analyzed sleep states independently from simultaneously recorded hippocampal depth electrodes and cortical scalp electrodes in eight human subjects who were implanted with depth electrodes for pharmacologically intractable epilepsy evaluation. We found that the neocortex and hippocampus could be in nonsimultaneous states, on average, one-third of the night and that the hippocampus often led in asynchronous state transitions. Nonsimultaneous bout lengths varied from 30 s to over 30 min. These results call into question the conclusions of studies, across phylogeny, that measure only surface cortical state but seek to assess the functions and drivers of sleep states throughout the brain. [ABSTRACT FROM AUTHOR]

The common age-dependent West syndrome can be diagnosed accurately by electroencephalogram (EEG), but its pathogenesis and evolution remain unclear. Existing research mainly aims at the study of West seizure markers in time/frequency domain, while less literature uses a graph-theoretic approach to analyze changes among different brain regions. In this paper, the scalp EEG based functional connectivity (including Correlation, Coherence, Time Frequency Cross Mutual Information, Phase-Locking Value, Phase Lag Index, Weighted Phase Lag Index) and network topology parameters (including Clustering coefficient, Feature path length, Global efficiency, and Local efficiency) are comprehensively studied for the prognostic analysis of the West episode cycle. The scalp EEGs of 15 children with clinically diagnosed string spasticity seizures are used for prospective study, where the signal is divided into pre-seizure, seizure, and post-seizure states in 5 typical brain wave rhythm frequency bands (δ (1–4 Hz), θ (4–8 Hz), α (8–13 Hz), β (13–30 Hz), and γ (30–80 Hz)) for functional connectivity analysis. The study shows that recurrent West seizures weaken connections between brain regions responsible for cognition and intelligence, while brain regions responsible for information synergy and visual reception have greater variability in connectivity during seizures. It is observed that the changes in β and γ frequency bands of the multiband brain network connectivity patterns calculated by Corr and WPLI can be preliminarily used as judgment of seizure cycle changes in West syndrome. [ABSTRACT FROM AUTHOR]

High‐frequency oscillations (HFO) in scalp EEG are a new and promising noninvasive epilepsy biomarker, providing added prognostic value, particularly in pediatric lesional epilepsy. However, it is unclear if lesion characteristics, such as lesion volume, depth, type, and localization, impact scalp HFO rates. We analyzed scalp EEG from 13 children and adolescents with focal epilepsy associated with focal cortical dysplasia (FCD), low‐grade tumors, or hippocampal sclerosis. We applied a validated automated detector to determine HFO rates in bipolar channels. We identified the lesion characteristics in MRI. Larger lesions defined by MRI volumetric analysis corresponded to higher cumulative scalp HFO rates (P =.01) that were detectable in a higher number of channels (P =.05). Both superficial and deep lesions generated HFO detectable in the scalp EEG. Lesion type (FCD vs tumor) and lobar localization (temporal vs extratemporal) did not affect scalp HFO rates in our study. Our observations support that all lesions may generate HFO detectable in scalp EEG, irrespective of their characteristics, whereas larger epileptogenic lesions generate higher scalp HFO rates over larger areas that are thus more accessible to detection. Our study provides crucial insight into scalp HFO detectability in pediatric lesional epilepsy, facilitating their implementation as an epilepsy biomarker in a clinical setting. [ABSTRACT FROM AUTHOR]

Objective: To describe the ictal scalp EEG patterns of occipital seizures (OS) and their spatiotemporal correlations with intracerebral occipital ictal discharges derived from simultaneous SEEG-EEG recordings., Methods: Patients with SEEG confirmed OS (14 OS from 8 patients) were selected from an epilepsy surgery center and were monitored 3-10 days using simultaneous scalp EEG and SEEG recordings., Results: On scalp EEG, the most common onset patterns were background activity suppression (28.6 %) and high amplitude slow wave corresponding to intracerebral DC-shift (28.6 %) and occurred with a median delay of 0 s after intra-cerebral onset. The initial discharge involved occipital electrodes in only 50 % of the seizures (7/14) with additional basal temporal (8/14) or parietal electrodes (5/14). The onset was ipsilateral to the intra-cerebral onset zone in 71.4 % of seizures and bilateral in the remaining (28.6 %). The most common propagation pattern was either unilateral (50 %) or bilateral (50 %) and a rhythmic slow activity (66.7 %). Different OS subtypes display distinct scalp EEG patterns., Conclusion: Scalp EEG accurately determines intra-cerebral seizure onset time in OS and has good lateralizing value. However, initial scalp modification does not always involves occipital electrodes and the second modification is well lateralizing in only 50 % of seizures., Significance: This study describes will help clinicians to better identify OS during video EEG and better plan intra-cerebral explorations for epilepsy surgery., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Objective: Corpus callosotomy (CC) is one of the palliative epilepsy surgical procedures available for nonlesional generalized epilepsy, but it is more invasive than other palliative surgical procedures. The main challenge is proper selection of suitable patients for CC. Coherence analysis is a method for evaluating brain connectivity, but the correlation between preoperative coherence and surgical outcomes has not previously been clarified. The authors aimed to evaluate correlations between preoperative interhemispheric coherence and surgical outcome in patients with nonlesional generalized epilepsy., Methods: This retrospective study investigated patients with nonlesional generalized epilepsy who underwent total CC. The authors collected data for patients with good seizure outcome (Oguni classification A or B) and bad seizure outcome (Oguni classification D). For coherence analysis, the authors selected a period without interictal discharges. Preoperative interhemispheric coherence values from 8 pairs of symmetrically opposite scalp electrodes were computed across 5 frequency bands. Then, the authors evaluated correlations between coherence and surgical outcomes., Results: Forty patients were included (19 males and 21 females). The mean (range) age at the time of surgery was 5.1 (1-18) years. Seizure outcomes were good in 15 patients and bad in the other 25 patients. Age at onset of epilepsy, duration of epilepsy before surgery, age at time of surgery, and presence of epileptic spasm did not differ significantly between patients with good and bad seizure outcomes (p = 0.36, p = 0.14, p = 0.10, and p = 0.20, respectively). Significant correlations were identified between higher Fp1-Fp2 interhemispheric coherence values in the delta, theta, and alpha frequency bands and bad surgical outcomes (p = 0.0397, p = 0.0322, and p = 0.0476, respectively). The receiver operating characteristic curves of the Fp1-Fp2 coherence values in these frequency bands showed areas under the curve of 67%, 69%, and 67%, respectively. The optimal cutoff values for Fp1-Fp2 interhemispheric coherence to predict surgical outcomes were 55.6 for delta (66.7% sensitivity and 72.0% specificity), 55.9 for theta (60.0% sensitivity and 76.0% specificity), and 50.3 for alpha (53.3% sensitivity and 84.0% specificity)., Conclusions: This is the first study to identify potential predictive factors for surgical outcomes based on preoperative interhemispheric coherence in nonlesional generalized epilepsy. Higher coherence between Fp1-Fp2 in the delta, theta, and alpha frequencies correlated with bad seizure outcome after CC.

Objectives: Malformations of cortical development (MCDs) are common causes of drug-resistant epilepsy. The mechanisms underlying the associated epileptogenesis and ictogenesis remain poorly elucidated. EEG can help in understanding these mechanisms. We systematically reviewed studies reporting scalp or intracranial EEG features of MCDs to characterise interictal and seizure-onset EEG patterns across different MCD types. Methods: We conducted a systematic review in accordance with PRISMA guidelines. MEDLINE, PubMed, and Cochrane databases were searched for studies describing interictal and seizure-onset EEG patterns in MCD patients. A classification framework was implemented to group EEG features into 20 predefined patterns, comprising nine interictal (five, scalp EEG; four, intracranial EEG) and 11 seizure-onset (five, scalp EEG; six, intracranial EEG) patterns. Logistic regression was used to estimate the odds ratios (OR) of each seizure-onset pattern being associated with specific MCD types. Results: Our search yielded 1682 studies, of which 27 comprising 936 MCD patients were included. Of the nine interictal EEG patterns, five (three, scalp EEG; two, intracranial EEG) were detected in ≥2 MCD types, while four (rhythmic epileptiform discharges type 1 and type 2 on scalp EEG; repetitive bursting spikes and sporadic spikes on intracranial EEG) were seen only in focal cortical dysplasia (FCD). Of the 11 seizure-onset patterns, eight (three, scalp EEG; five, intracranial EEG) were found in ≥2 MCD types, whereas three were observed only in FCD (suppression on scalp EEG; delta brush on intracranial EEG) or tuberous sclerosis complex (TSC; focal fast wave on scalp EEG). Among scalp EEG seizure-onset patterns, paroxysmal fast activity (OR = 0.13; 95% CI: 0.03–0.53; p = 0.024) and repetitive epileptiform discharges (OR = 0.18; 95% CI: 0.05–0.61; p = 0.036) were less likely to occur in TSC than FCD. Among intracranial EEG seizure-onset patterns, low-voltage fast activity was more likely to be detected in heterotopia (OR = 19.3; 95% CI: 6.22–60.1; p

Objective: Focal cortical dysplasia (FCD) represents an essential cause of drug‐resistant epilepsy with surgery as an effective treatment option. This study aimed to identify the important predictors of favorable surgical outcomes and the impact of the interictal scalp electroencephalogram (EEG) patterns in predicting postsurgical seizure outcomes. Methods: We retrospectively evaluated 210 consecutive patients between 2015 and 2019. They were diagnosed with FCD by pathology, underwent resection, and had at least one year of postsurgical follow‐up. Predictors of seizure freedom were analyzed. Results: Based on the information at the latest follow‐up, seizure outcome was classified as Engel Class I (seizure‐free) in 81.4% and Engel Class II‐IV (non‐seizure‐free) in 18.6% of patients. There were 43, 105, and 62 cases of FCD type I, type II, and type III, respectively. The interictal EEG showed a repetitive discharge pattern (REDP) in 87 (41.4%) patients, polyspike discharge pattern (PDP) in 41 (19.5%), and the coexistence of REDP and PDP in the same location in 32 (15.2%) patients. The analyzed patterns in order of frequency were repetitive discharges lasting 5 seconds or more (32.4%); polyspikes (16.7%); RED type 1 (11.4%); continuous epileptiform discharges occupying >80% of the recording (11.4%); RED type 2 (6.2%); brushes (3.3%); focal, fast, continuous spikes (2.4%); focal fast rhythmic epileptiform discharges (1.43%); and frequent rhythmic bursting epileptiform activity (1.4%). The coexistence of REDP and PDP in the same location on scalp EEG and complete resection of the assumed epileptogenic zone (EZ) was independently associated with favorable postsurgical prognosis. Significance: Resective epilepsy surgery for intractable epilepsy caused by FCD has favorable outcomes. Interictal scalp EEG patterns were revealed to be predictive of excellent surgical outcomes and may help clinical decision‐making and enable better presurgical evaluation. [ABSTRACT FROM AUTHOR]

Modern electroencephalographic (EEG) technology contributed to the appreciation that the EEG signal outside the classical Berger frequency band contains important information. In epilepsy, research of the past decade focused particularly on interictal high-frequency oscillations (HFOs) > 80 Hz. The first large application of HFOs was in the context of epilepsy surgery. This is now followed by other applications such as assessment of epilepsy severity and monitoring of antiepileptic therapy. This article reviews the evidence on the clinical use of HFOs in epilepsy with an emphasis on the latest developments. It highlights the growing literature on the association between HFOs and postsurgical seizure outcome. A recent meta-analysis confirmed a higher resection ratio for HFOs in seizure-free versus non-seizure-free patients. Residual HFOs in the postoperative electrocorticogram were shown to predict epilepsy surgery outcome better than preoperative HFO rates. The review further discusses the different attempts to separate physiological from epileptic HFOs, as this might increase the specificity of HFOs. As an example, analysis of sleep microstructure demonstrated a different coupling between HFOs inside and outside the epileptogenic zone. Moreover, there is increasing evidence that HFOs are useful to measure disease activity and assess treatment response using noninvasive EEG and magnetoencephalography. This approach is particularly promising in children, because they show high scalp HFO rates. HFO rates in West syndrome decrease after adrenocorticotropic hormone treatment. Presence of HFOs at the time of rolandic spikes correlates with seizure frequency. The time-consuming visual assessment of HFOs, which prevented their clinical application in the past, is now overcome by validated computer-assisted algorithms. HFO research has considerably advanced over the past decade, and use of noninvasive methods will make HFOs accessible to large numbers of patients. Prospective multicenter trials are awaited to gather information over long recording periods in large patient samples.

Objective: To develop a novel software method (AR2) for reducing muscle contamination of ictal scalp electroencephalogram (EEG), and validate this method on the basis of its performance in comparison to a commercially available software method (AR1) to accurately depict seizure-onset location. Methods: A blinded investigation used 23 EEG recordings of seizures from 8 patients. Each recording was uninterpretable with digital filtering because of muscle artifact and processed using AR1 and AR2 and reviewed by 26 EEG specialists. EEG readers assessed seizure-onset time, lateralization, and region, and specified confidence for each determination. The two methods were validated on the basis of the number of readers able to render assignments, confidence, the intra-class correlation (ICC), and agreement with other clinical findings. Results: Among the 23 seizures, two-thirds of the readers were able to delineate seizure-onset time in 10 of 23 using AR1, and 15 of 23 using AR2 (p

In recent years, the Burden of Amplitudes and Epileptiform Discharges (BASED) score has been used as a reliable, accurate, and feasible electroencephalogram (EEG) grading scale for infantile spasms. However, manual EEG annotation is, in general, very time-consuming, and BASED scoring is no exception. Convolutional neural networks (CNNs) have proven their great potential in many EEG classification problems. However, very few research studies have focused on the use of CNNs for BASED scoring, a challenging but vital task in the diagnosis and treatment of infantile spasms. This study proposes an automatic BASED scoring framework using EEG and a deep CNN. The feasibility of using CNN for automatic BASED scoring was investigated in 36 patients with infantile spasms by annotating their long-term EEG data with four levels of the BASED score (scores 5, 4, 3, and ≤2). In the validation set, the accuracy was 96.9% by applying a multi-layer CNN to classify the EEG data as a 4-label problem. The extensive experiments have demonstrated that our proposed approach offers high accuracy and, hence, is an important step toward an automatic BASED scoring algorithm. To the best of our knowledge, this is the first attempt to use a CNN to construct a BASED-based scoring model.

Motion artifacts contribute complexity in acquiring clean electroencephalography (EEG) data. It is one of the major challenges for ambulatory EEG. The performance of mobile health monitoring, neurological disorders diagnosis and surgeries can be significantly improved by reducing the motion artifacts. Although different papers have proposed various novel approaches for removing motion artifacts, the datasets used to validate those algorithms are questionable. In this paper, a unique EEG dataset was presented where ten different activities were performed. No such previous EEG recordings using EMOTIV EEG headset are available in research history that explicitly mentioned and considered a number of daily activities that induced motion artifacts in EEG recordings. Quantitative study shows that in comparison to correlation coefficient, the coherence analysis depicted a better similarity measure between motion artifacts and motion sensor data. Motion artifacts were characterized with very low frequency which overlapped with the Delta rhythm of the EEG. Also, a general wavelet transform based approach was presented to remove motion artifacts. Further experiment and analysis with more similarity metrics and longer recording duration for each activity is required to finalize the characteristics of motion artifacts and henceforth reliably identify and subsequently remove the motion artifacts in the contaminated EEG recordings. [ABSTRACT FROM AUTHOR]

• Scalp high frequency oscillation (HFO) rates are higher in N3 than N2 or REM sleep. • Scalp HFO rates are higher in the first sleep cycle. • N3 data intervals of 10 min are required for HFO analysis. High frequency oscillations (HFO) in scalp EEG are a new and promising epilepsy biomarker. However, considerable fluctuations of HFO rates have been observed through sleep stages and cycles. Here, we aimed to identify the optimal timing within sleep and the minimal data length for sensitive and reproducible HFO detection. We selected 16 whole-night scalp EEG recordings of paediatric patients with a focal structural epilepsy. We used an automated clinically validated HFO detector to determine HFO rates (80–250 Hz). We evaluated the reproducibility of HFO detection across intervals. HFO rates were higher in N3 than in N2 and REM (rapid eye movement) sleep and highest in the first sleep cycle, decreasing with time in sleep. In N3 sleep, the median reliability of HFO detection increased from 67% (interquartile range: iqr 57) to 78% (iqr 59) to 100% (iqr 70%) for 5-, 10-, and 15-min data intervals, improving significantly (p = 0.004, z = 2.9) from 5 to 10 min but not from 10 to 15 min. We identified the first N3 sleep stage as the most sensitive time window for HFO rate detection. At least 10 min N3 data intervals are required and sufficient for reliable measurements of HFO rates. Our study provides a robust and reliable framework for scalp HFO detection that may facilitate their implementation as an EEG biomarker in paediatric epilepsy. [ABSTRACT FROM AUTHOR]

Epilepsy is a severe neurological disease with high prevalence and morbidity worldwide. The unpredictability of seizures prevents physicians from tailoring drugs and therapies. Recent non-invasive seizure prediction research has not improved the overall quality of life for patients. Therefore, new research studies on seizure prediction must integrate data, embedded devices, and algorithms. For a seizure prediction system to emerge as a feasible solution, we must address a reduction in EEG scalp electrode channels, along with a decrease in computational resources to train the time-series signal. In this work, we propose an optimized patient-specific channel reduction for seizure prediction using Model Agnostic Meta-Learning (MAML) applied to a Deep Neural Network (DNN). We selected and optimized the number of channels from each of the 23 subjects of the CHB-MIT Dataset. The feature vectors are extracted using Ensemble Empirical Mode Decomposition (EEMD) and Sequential Feature Selection (SFS). We implemented the MAML model to classify the small EEG data generated from the reduced number of subject-dependent electrodes. The experiment results yield an average sensitivity and specificity of 91% and 90%, respectively. Our study demonstrates that MAML is a promising approach to learn EEG patterns to predict epileptic seizures with few EEG scalp electrodes.

Objective: The auditory steady-state response (ASSR) is commonly used in clinical pediatric audiology in order to provide an electrophysiological estimate of hearing threshold, and has the potential to be used in unsupervised mobile EEG applications. Enhancement of the ASSR amplitude through optimization of the stimulation and recording methods shortens the required testing time or reduce the offset between the electrophysiological and behavioral thresholds. Here, we investigate the spatial distribution of the ASSR to broadband chirp stimuli across a wide range of repetition rates on the scalp and in the ears. Moreover, the ASSR amplitude is compared across repetition rates for commonly used electrode configurations. Methods: ASSR to chirp stimuli with repetition rates from 6-198 Hz was recorded using scalp EEG and high-density ear-EEG. Results: The distributions of the ASSR amplitude and phase were found to be dependent on the chirp repetition rate across the scalp, but independent of repetition rate in the ears. The normal drop in ASSR SNR for high repetition rates seen for click and pure tone stimuli was not found for chirp stimuli. Instead, the ASSR SNRs for chirp stimuli at high repetition rates (95–198 Hz) were found to be comparable to that found at 40 Hz for scalp EEG and even higher than 40 Hz ASSR for ear-EEG. Conclusion: Based on the results, use of chirp stimuli with high repetition rates (95–198 Hz) is advantageous for multiple stimulus ASSR recording in both clinical practice and mobile real-life applications. [ABSTRACT FROM AUTHOR]

Automatic seizure detection technology is of great significance to reduce workloads of neurologists for epilepsy diagnosis and treatments. Imbalanced classification is a challenge in seizure detection from long-term continuous EEG recordings, as the durations of the seizure events are much shorter than the non-seizure periods. An imbalanced deep learning model is proposed in this paper to improve the performance of seizure detection. To modify imbalanced EEG data distribution, a generative adversarial network (GAN) that is a strong candidate for data enhancement is built to produce the seizure-period EEG data used for forming a more balanced training set. Next, a pyramidal one-dimensional convolutional neural network (1DCNN) is designed to deal with 1D EEG signals and trained on the augmented training set that consists of both original and generated EEG data. Compared to the conventional 2DCNNs, the deep architecture of the 1DCNN reduces the training parameters so as to greatly increase the training speed. The proposed method is evaluated on three publicly available EEG databases. After data augmentation by the GAN, the designed 1DCNN shows much better classification for seizure detection, achieving competitive results over the three EEG databases, which demonstrates the generalizability of this method across different databases. Comparison with other published methods indicates its enhanced detection performance for imbalanced EEG data. [ABSTRACT FROM AUTHOR]

Visual inspection of long-term electroencephalography (EEG) is a tedious task for physicians in neurology. Based on bidirectional gated recurrent unit (Bi-GRU) neural network, an automatic seizure detection method is proposed in this paper to facilitate the diagnosis and treatment of epilepsy. Firstly, wavelet transforms are applied to EEG recordings for filtering pre-processing. Then the relative energies of signals in several particular frequency bands are calculated and inputted into Bi-GRU network. Afterwards, the outputs of Bi-GRU network are further processed by moving average filtering, threshold comparison and seizure merging to generate the discriminant results that the tested EEG belong to seizure or not. Evaluated on CHB-MIT scalp EEG database, the proposed seizure detection method obtained an average sensitivity of 93.89% and an average specificity of 98.49%. 124 out of 128 seizures were correctly detected and the achieved average false detection rate was 0.31 per hour on 867.14 h testing data. The results show the superiority of Bi-GRU network in seizure detection and the proposed detection method has a promising potential in the monitoring of long-term EEG. [ABSTRACT FROM AUTHOR]

Being one of the most prevalent neurological disorders, epilepsy affects the lives of patients through the infrequent occurrence of spontaneous seizures. These seizures can result in serious injuries or unexpected deaths in individuals due to accidents. So, there exists a crucial need for an automatic prediction of epileptic seizures to alert the patients well before the onset of seizures, enabling them to have a healthier quality of life. In this era, the Internet of Things (IoT) technologies are being used in a cloud-fog integrated environment to address such healthcare challenges using deep learning approaches. The present paper also proposes a smart health monitoring approach for automated prediction of epileptic seizures using deep learning-based spectral analysis of EEG signals. This approach processes EEG signals using filtering, segmentation into short duration segments and spectral-domain transformation. These signals are then analysed spectrally by separating them into several spectral bands, such as delta, theta, alpha, beta, and sub-bands of gamma. Furthermore, the mean spectral amplitude and spectral power features are retrieved from each spectral band to characterize various seizure states, which are fed to the proposed LSTM and CNN models. The results of the proposed CNN model show a maximum accuracy of 98.3% and 97.4% to obtain a binary classification of preictal and interictal seizure states for two different spectral band combinations respectively. Thus, the proposed CNN architecture accompanied by spectral analysis of EEG signals provides a viable method for reliable and real-time prediction of epileptic seizures. [ABSTRACT FROM AUTHOR]

Epilepsy is a very common neurological disease that has affected more than 65 million people worldwide. In more than 30 % of the cases, people affected by this disease cannot be cured with medicines or surgery. However, predicting a seizure before it actually occurs can help in its prevention; through therapeutic intervention. Studies have observed that abnormal activity inside the brain begins a few minutes before the start of a seizure, which is known as preictal state. Many researchers have tried to find a way for predicting this preictal state of a seizure but an effective prediction in terms of high sensitivity and specificity still remains a challenge. The current study, proposes a seizure prediction system that employs deep learning methods. This method includes preprocessing of scalp EEG signals, automated features extraction; using convolution neural network and classification with the support of vector machines. The proposed method has been applied on 24 subjects of scalp EEG dataset of CHBMIT resulting in successfully achieving an average sensitivity and specificity of 92.7% and 90.8% respectively.

Objective: This study aims to evaluate the role of scalp electroencephalography (EEG; ictal and interictal patterns) in predicting resective epilepsy surgery outcomes. We use the data to further develop a nomogram to predict seizure freedom. Methods: We retrospectively reviewed the scalp EEG findings and clinical data of patients who underwent surgical resection at three epilepsy centers. Using both EEG and clinical variables categorized into 13 isolated candidate predictors and 6 interaction terms, we built a multivariable Cox proportional hazards model to predict seizure freedom 2 years after surgery. Harrell's step‐down procedure was used to sequentially eliminate the least‐informative variables from the model until the change in the concordance index (c‐index) with variable removal was less than 0.01. We created a separate model using only clinical variables. Discrimination of the two models was compared to evaluate the role of scalp EEG in seizure‐freedom prediction. Results: Four hundred seventy patient records were analyzed. Following internal validation, the full Clinical + EEG model achieved an optimism‐corrected c‐index of 0.65, whereas the c‐index of the model without EEG data was 0.59. The presence of focal to bilateral tonic‐clonic seizures (FBTCS), high preoperative seizure frequency, absence of hippocampal sclerosis, and presence of nonlocalizable seizures predicted worse outcome. The presence of FBTCS had the largest impact for predicting outcome. The analysis of the models' interactions showed that in patients with unilateral interictal epileptiform discharges (IEDs), temporal lobe surgery cases had a better outcome. In cases with bilateral IEDs, abnormal magnetic resonance imaging (MRI) predicted worse outcomes, and in cases without IEDs, patients with extratemporal epilepsy and abnormal MRI had better outcomes. Significance: This study highlights the value of scalp EEG, particularly the significance of IEDs, in predicting surgical outcome. The nomogram delivers an individualized prediction of postoperative outcome, and provides a unique assessment of the relationship between the outcome and preoperative findings. [ABSTRACT FROM AUTHOR]

• In patients with epilepsy, intracranial correlates of small sharp spikes (SSS) are spike-/polyspike-bursts. • The hippocampus is the principal, yet not only generator of SSS. • SSS cerebral sources are topographically related to the seizure onset zone. To identify cortical correlates of scalp small sharp spikes (SSS) using simultaneous scalp and intracranial EEG recordings. Patients were retrospectively evaluated based on a database of intracranial long-term recordings at the Epilepsy Center Freiburg. Inclusion criteria were: simultaneous recordings with intracranial and scalp EEGs and the presence of at least five unequivocal SSS in the scalp EEG. Intracranial recordings were analyzed regarding the co-occurring intracranial potentials during scalp SSS. 33 patients, aged 9-60y, 17 females, fulfilled the above-mentioned criteria. Almost all patients had intracranial SSS correlates in the form of spike/polyspike-waves in the temporal lobe, predominantly in the hippocampus (24/28), less frequently involving the amygdala (5/29), temporal basal (3/18), lateral neocortical (4/32), entorhinal cortices (1/12), and the parietal lobe (2/13). Amplitudes of intrahippocampal spikes or polyspikes co-occurring with SSS were significantly higher than intracranial discharges without scalp correlates. In 45% of patients, intracranial spikes accompanying SSS were located within the seizure onset zone (SOZ). Our results strongly support an epileptic origin of SSS and provide evidence about their heterogenous generators. This study suggests that SSS cannot with certainty be classified as "benign" but rather considered as one of the EEG manifestations of focal epilepsy. [ABSTRACT FROM AUTHOR]

Conceptual knowledge allows the categorisation of items according to their meaning beyond their physical similarities. This ability to respond to different stimuli (e.g., a leek, a cabbage, etc.) based on similar semantic representations (e.g., belonging to the vegetable category) is particularly important for language processing, because word meaning and the stimulus form are unrelated. The neural basis of this core human ability is debated and is complicated by the strong reliance of most neural measures on explicit tasks, involving many non-semantic processes. Here we establish an implicit method, i.e., fast periodic visual stimulation (FPVS) coupled with electroencephalography (EEG), to study neural conceptual categorisation processes with written word stimuli. Fourteen neurotypical participants were presented with different written words belonging to the same semantic category (e.g., different animals) alternating at 4 Hz rate. Words from a different semantic category (e.g., different cities) appeared every 4 stimuli (i.e., at 1 Hz). Following a few minutes of recording, objective electrophysiological responses at 1 Hz, highlighting the human brain's ability to implicitly categorize stimuli belonging to distinct conceptual categories, were found over the left occipito-temporal region. Topographic differences were observed depending on whether the periodic change involved living items, associated with relatively more ventro-temporal activity as compared to non-living items associated with relatively more dorsal posterior activity. Overall, this study demonstrates the validity and high sensitivity of an implicit frequency-tagged marker of word-based semantic memory abilities.

IntroductionDespite all the efforts for optimizing epilepsy management in children over the past decades, there is no clear consensus regarding whether to treat or not to treat epileptiform discharges (EDs) after a first unprovoked seizure or the optimal duration of therapy with anti-seizure medication (ASM). It is therefore highly needed to find markers on scalp electroencephalogram (EEG) that can help identify pathological EEG discharges that require treatment.Aim of the studyThis retrospective study aimed to identify whether the coexistence of ripples/high-frequency oscillations (HFOs) with interictal EDs (IEDs) in routinely acquired scalp EEG is associated with a higher risk of seizure recurrence and could be used as a prognostic marker.Methods100 children presenting with new onset seizure to Children’s Medical Center- Dallas during 2015–2016, who were not on ASM and had focal EDs on an awake and sleep EEG recorded with sample frequency of 500 HZ, were randomly identified by database review. EEGs were analyzed blinded to the data of the patients. HFOs were visually identified using review parameters including expanded time base and adjusted filter settings.ResultsThe average age of patients was 6.3 years (±4.35 SD). HFOs were visually identified in 19% of the studied patients with an inter-rater reliability of 99% for HFO negative discharges and 78% agreement for identification of HFOs. HFOs were identified more often in the younger age group; however, they were identified in 11% of patients >5 years old. They were more frequently associated with spikes than with sharp waves and more often with higher amplitude EDs. Patients with HFOs were more likely to have a recurrence of seizures in the year after the first seizure (P < 0.05) and to continue to have seizures after 2 years (P < 0.0001). There was no statistically significant difference between the two groups with regards to continuing ASM after 2 years.ConclusionIncluding analysis for HFOs in routine EEG interpretation may increase the yield of the study and help guide the decision to either start or discontinue ASM. In the future, this may also help to identify pathological discharges with deleterious effects on the growing brain and set a new target for the management of epilepsy.