Specific EEG Features Research Articles

Circadian and sleep–wake homeostatic modulation of EEG activity during sleep and wakefulness

AbstractThe human sleep–wake cycle is regulated by two distinct processes: the circadian timing system (CTS) and the sleep–wake homeostatic (SWH) process. The CTS is driven by a small region in the anterior hypothalamus of the brain, which is known as the “circadian clock.” By contrast, the SWH can be conceptualized as an hourglass, whereby sleep pressure builds up during waking hours and is released during sleep. In contrast to the CTS, there is no specific brain region that controls this hourglass process. A complex modulation of these two processes affects electroencephalographic (EEG) activity throughout the 24‑h day, resulting in the emergence of specific EEG features. These features can be classified into three categories: those that show clear circadian patterns, those that are predominantly influenced by the homeostatic process, and those that show a combination of both. This review describes the EEG features quantified by spectral analysis during sleep and wakefulness derived from specific human study protocols, which enable the separation of the influence of the circadian clock and the sleep–wake hourglass process. Second, the potential for circadian and SWH modulation to influence interictal activity and seizure occurrence will be discussed, along with its implications for the diagnosis, treatment, and seizure prediction and prevention.

Specific feature selection in wearable EEG-based transducers for monitoring high cognitive load in neurosurgeons

The electroencephalographic (EEG) features for discriminating high and low cognitive load associated with fine motor activity in neurosurgeons were identified by combining wearable transducers and Machine Learning (ML). To date, in the literature, the specific impact of fine-motor tasks on surgeons’ cognitive load is poorly investigated and studies rely on the EEG features selected for cognitive load induced by other types of tasks (driving and flight contexts). In this study, the specific EEG features for detecting cognitive load associated with fine motor activity in neurosurgeons are investigated. Six neurosurgeons were EEG monitored by means of an eight-dry-channel EEG transducer during the execution of a standardized test of fine motricity assessment. The most informative EEG features of the cognitive load induced by fine motor activity were identified by exploiting the algorithm Sequential Feature Selector. In particular, five ML classifiers maximized their classification accuracy having as input the relative alpha power in Fz, O1, and O2, computed on 2-s epochs with an overlap of 50 %. These results demonstrate the feasibility of ML-supported wearable EEG solutions for monitoring persistent high cognitive load over time and alerting healthcare management.

Trial of Brain–Computer Interface for Continuous Motion Using Electroencephalography and Electromyography

Most BCI systems used in neurorehabilitation detect EEG features indicating motor intent based on machine learning, focusing on repetitive movements, such as limb flexion and extension. These machine learning methods require large datasets and are time consuming, making them unsuitable for same-day rehabilitation training following EEG measurements. Therefore, we propose a BMI system based on fuzzy inference that bypasses the need for specific EEG features, introducing an algorithm that allows patients to progress from measurement to training within a few hours. Additionally, we explored the integration of electromyography (EMG) with conventional EEG-based motor intention estimation to capture continuous movements, which is essential for advanced motor function training, such as skill improvement. In this study, we developed an algorithm that detects the initial movement via EEG and switches to EMG for subsequent movements. This approach ensures real-time responsiveness and effective handling of continuous movements. Herein, we report the results of this study.

Research on Nuclear Power Plant Operational Task Complexity Assessment Method Based on Group Decision Making and Deep Learning of EEG Signals

This study aims to explore the correlation between the operational task complexity of nuclear power plant (NPP) operators and electroencephalogram (EEG) features. Initially, we segmented EEG signals according to operational steps and extracted a total of 120 time domain, frequency domain, and time-frequency domain features. Subsequently, we applied an adaptive principal component analysis (PCA) dimensionality reduction method to process the features. On the other hand, three experts were invited to evaluate the complexity of the operational tasks, and their evaluation data were synthesized using a group decision-making approach. A correlation analysis was performed between these data and the PCA-reduced feature data, identifying the features with the highest correlation coefficient for each participant. Then we built a long short-term memory model with the data of the first group of participants to predict the task complexity value and tested it with the data of the second group of participants. Testing the model with data from the second group yielded favorable results, with a training set mean squared error (MSE) of 0.025 and a testing set MSE of 0.078. The results of this study indicate a significant correlation between specific EEG features and task complexity in the operational tasks of NPP operators. The model established through a combination of group decision making and machine learning methods effectively predicted the task complexity levels for operators in different operational tasks. This research provides a new perspective on NPP operators’ cognitive load and operational tasks, holding practical significance for operator training and workload management.

Admission Clinical and EEG Features Associated With Mortality and Long-term Neurologic and Cognitive Outcomes in Pediatric Cerebral Malaria.

For children with cerebral malaria, mortality is high, and in survivors, long-term neurologic and cognitive dysfunctions are common. While specific clinical factors are associated with death or long-term neurocognitive morbidity in cerebral malaria, the association of EEG features with these outcomes, particularly neurocognitive outcomes, is less well characterized. In this prospective cohort study of 149 children age 6 months to 12 years who survived cerebral malaria in Kampala, Uganda, we evaluated whether depth of coma, number of clinical seizures, or EEG features during hospitalization were associated with mortality during hospitalization, short-term and long-term neurologic deficits, or long-term cognitive outcomes (overall cognition, attention, memory) over the 2-year follow-up. Higher Blantyre or Glasgow Coma Scores (BCS and GCS, respectively), higher background voltage, and presence of normal reactivity on EEG were each associated with lower mortality. Among clinical and EEG features, the presence of >4 seizures on admission had the best combination of negative and positive predictive values for neurologic deficits in follow-up. In multivariable modeling of cognitive outcomes, the number of seizures and specific EEG features showed independent association with better outcomes. In children younger than 5 years throughout the study, seizure number and presence of vertex sharp waves were independently associated with better posthospitalization cognitive performance, faster dominant frequency with better attention, and higher average background voltage and faster dominant background frequency with better associative memory. In children younger than 5 years at CM episode but 5 years or older at cognitive testing, seizure number, background dominant frequency, and the presence of vertex sharp waves were each associated with changes in cognition, seizure number and variability with attention, and seizure number with working memory. In children with cerebral malaria, seizure number is strongly associated with the risk of long-term neurologic deficits, while seizure number and specific EEG features (average background voltage, dominant rhythm frequency, presence of vertex sharp waves, presence of variability) are independently associated with cognitive outcomes. Future studies should evaluate the predictive value of these findings.

EEG Features for Driver’s Mental Fatigue Detection: A Preliminary Work

Mental fatigue is one of the most typical human infirmities, resulting from an overload of work and lack of sleep which can reduce one’s intellectual resources. Different EEG features have been studied for detecting mental fatigue. This paper characterizes mental fatigue through the understanding of human EEG features for safe driving behaviour and to create an overview of the potential EEG features which are related to mental fatigue. A narrative review approach is employed for describing the neural activity of the human brain in mental fatigue. Specific EEG features in relation to driving tasks, relation to different EEG band waves, pre-processing and feature extraction methods are discussed. From this preliminary work, the increase of parietal alpha power seems to characterize the driver’s mental fatigue in most of the studies. We searched public EEG repositories for identifying potential data sources for our initial study. Finally, we propose a conceptual model that has potentials for identifying mental weariness. In conclusion, future works may involve the identification of other EEG features of higher importance for generalization across study conditions

QEEG‐based differentiation between Alzheimer’s disease with or without alpha synucleinopathy

AbstractBackgroundAs existence of alpha synucleniopathy could affect progression of cognitive impairment in Alzheimer’s disease (AD), it’s very important to differentiate comorbidity of alpha synucleniopathy in AD to predict progression. To develop affordable EEG‐based discriminating machine‐learning(ML) algorithm for existence of alpha synucleniopathy, we explore differences between AD with vs without it.MethodBased on pattern of cognitive impairment and 3 types of PET scan [18F‐Florbetaben brain amyloid‐beta, FDG, DAT‐PET] , dementia due to Alzheimer’s disease [pure ADD] or Lewy Body (pure LBD) or mixed type (AD with LB disease) were clinically classified.We measured 19ch resting state EEG based on international 10‐20. Quantitative analysis of EEG was done by iSyncBrain®.ResultPure ADD showed the characteristics that general EEG slowing with low total power, relative delta enhancement and desynchronized alpha with low amplitude. Pure LBD showed the pattern of slow alpha peak frequency with intact synchronization, relatively higher EEG total power and frontotemporal theta enhancement. Compared to pure ADD, ADD with LB showed mixed pattern of ADD and LDB. Total power was higher, alpha wave showed intact synchronization but theta power was rather enhanced at frontal(Fz; p‐value < 0.05) and bilateral temporal(T3,5, T4,6; p‐value < 0.05) area that was reported as characteristic pattern of progressive type of AD by previous studies.ConclusionThese findings imply that ADD could have different EEG oscillation characteristics according to the Lewybody disease. Next step, we will develop the machine‐learning algorithm to discriminate the Lewybody disease based on specific EEG features and validate it.

Seizures in autoimmune encephalitis: specific features based on a systematic comparative study.

To highlight specific characteristics of seizure semiology and EEG features associated with different subtypes of autoimmune encephalitis (AE). We systematically reviewed the seizure semiology and all the EEG recordings from patients with AE managed in a tertiary referral centre for epilepsy and a neuro-intensive care unit. Each characteristic across the different subtypes of AE was compared by post hoc analysis. We identified 66 patients with anti-neuronal antibody-mediated AE or Rasmussen's encephalitis (RE) experiencing seizures, which were the most frequent symptom at onset. Anti-NMDAR and anti-LGI1 AE accounted for the majority of patients; 41% and 24%, respectively. We isolated specific semiological features, such as early tonic-clonic seizures (TCS) in anti-NMDAR AE, early mesial temporal lobe seizures with emotional symptoms in anti-GAD AE, somatosensory seizures in RE, and a lower frequency of TCS in anti-LGI1 AE. EEG analysis also provided additional insights into distinguishing the subtypes based on: (1) generalized rhythmic delta activity, which was more sensitive than extreme delta brush in identifying anti-NMDAR AE among all subtypes; and (2) temporal interictal epileptiform activity and temporal seizures on EEG in anti-GAD AE. We identified a new EEG pattern consisting of temporal low-voltage and periodic spikes associated with ipsilateral hippocampal abnormalities on MRI, which could be a sign of inflammatory mesial temporal involvement. Specific clinical and EEG features can be useful in guiding the diagnosis of a subtype of AE with acute symptomatic seizures, particularly before the results of anti-neuronal antibody testing are available.

A Study on Facial Expression Change Detection Using Machine Learning Methods with Feature Selection Technique

Along with the fourth industrial revolution, research in the biomedical engineering field is being actively conducted. Among these research fields, the brain–computer interface (BCI) research, which studies the direct interaction between the brain and external devices, is in the spotlight. However, in the case of electroencephalograph (EEG) data measured through BCI, there are a huge number of features, which can lead to many difficulties in analysis because of complex relationships between features. For this reason, research on BCIs using EEG data is often insufficient. Therefore, in this study, we develop the methodology for selecting features for a specific type of BCI that predicts whether a person correctly detects facial expression changes or not by classifying EEG-based features. We also investigate whether specific EEG features affect expression change detection. Various feature selection methods were used to check the influence of each feature on expression change detection, and the best combination was selected using several machine learning classification techniques. As a best result of the classification accuracy, 71% of accuracy was obtained with XGBoost using 52 features. EEG topography was confirmed using the selected major features, showing that the detection of changes in facial expression largely engages brain activity in the frontal regions.

Prolonged epileptic discharges predict seizure recurrence in JME: Insights from prolonged ambulatory EEG.

ObjectiveMarkers of seizure recurrence are needed to personalize antiseizure medication (ASM) therapy. In the clinical practice, EEG features are considered to be related to the risk of seizure recurrence for genetic generalized epilepsies (GGE). However, to our knowledge, there are no studies analyzing systematically specific EEG features as indices of ASM efficacy in GGE. In this study, we aimed at identifying EEG indicators of ASM responsiveness in Juvenile Myoclonic Epilepsy (JME), which, among GGE, is characterized by specific electroclinical features.MethodsWe compared the features of prolonged ambulatory EEG (paEEG, 22 h of recording) of JME patients experiencing seizure recurrence within a year (“cases”) after EEG recording, with those of patients with sustained seizure freedom for at least 1 year after EEG (“controls”). We included only EEG recordings of patients who had maintained the same ASM regimen (dosage and type) throughout the whole time period from the EEG recording up to the outcome events (which was seizure recurrence for the “cases”, or 1‐year seizure freedom for “controls”). As predictors, we evaluated the total number, frequency, mean and maximum duration of epileptiform discharges (EDs) and spike density (i.e. total EDs duration/artifact‐free EEG duration) recorded during the paEEG. The same indexes were assessed also in standard EEG (stEEG), including activation methods.ResultsBoth the maximum length and the mean duration of EDs recorded during paEEG significantly differed between cases and controls; when combined in a binary logistic regression model, the maximum length of EDs emerged as the only valid predictor. A cut‐off of EDs duration of 2.68 seconds discriminated between cases and controls with a 100% specificity and a 93% sensitivity. The same indexes collected during stEEG lacked both specificity and sensitivity.SignificanceThe occurrence of prolonged EDs in EEG recording might represent an indicator of antiepileptic drug failure in JME patients.

COVID-19 Continuous-EEG Case Series: A Descriptive Study.

Corona virus disease 2019 (COVID-19) refers to coronavirus disease secondary to SARS-CoV2 infection mainly affecting the human respiratory system. The SARS-CoV2 has been reported to have neurotropic and neuroinvasive features and neurological sequalae with wide range of reported neurological manifestations, including cerebrovascular disease, skeletal muscle injury, meningitis, encephalitis, and demyelination, as well as seizures and focal status epilepticus. In this case series, we analyzed the continuous video-EEGs of patients with COVID-19 infection to determine the presence of specific EEG features or epileptogenicity. All continuous video-EEG tracings done on SARS-CoV2-positive patients during a 2-week period from April 5, 2020, to April 19, 2020, were reviewed. The demographics, clinical characteristics, imaging, and EEG features were analyzed and presented. Of 23 patients undergoing continuous video-EEG, 16 were COVID positive and were included. Continuous video-EEG monitoring was ordered for "altered mental status" in 11 of 16 patients and for "clinical seizure" in 5 of 16 patients. None of the patients had seizures or status epilepticus as a presenting symptom of COVID-19 infection. Instead, witnessed clinical seizures developed as results of COVID-19-related medical illness(es): anoxic brain injury, stroke/hemorrhage, lithium (Li) toxicity (because of kidney failure), hypertension, and renal disease. Three patients required therapeutic burst suppression because of focal nonconvulsive status epilepticus, status epilepticus/myoclonus secondary to anoxic injury from cardiac arrest, and one for sedation (and with concomitant EEG abnormalities secondary to Li toxicity). In this observational case series of 16 patients with COVID-19 who were monitored with continuous video-EEG, most patients experienced a nonspecific encephalopathy. Clinical seizures and electrographic status epilepticus were the second most commonly observed neurological problem.

Prediction of regaining consciousness despite an early epileptiform EEG after cardiac arrest.

After cardiac arrest (CA), epileptiform EEG, occurring in about 1/3 of patients, often but not invariably heralds poor prognosis. We tested the hypothesis that a combination of specific EEG features identifies patients who may regain consciousness despite early epileptiform patterns. We retrospectively analyzed a registry of comatose patients post-CA (2 Swiss centers), including those with epileptiform EEG. Background and epileptiform features in EEGs 12-36 hours or 36-72 hours from CA were scored according to the American Clinical Neurophysiology Society nomenclature. Best Cerebral Performance Category (CPC) score within 3 months (CPC 1-3 vs 4-5) was the primary outcome. Significant EEG variables were combined in a score assessed with receiver operating characteristic curves, and independently validated in a US cohort; its correlation with serum neuron-specific enolase (NSE) was also tested. Of 488 patients, 107 (21.9%) had epileptiform EEG <72 hours; 18 (17%) reached CPC 1-3. EEG 12-36 hours background continuity ≥50%, absence of epileptiform abnormalities (p < 0.00001 each), 12-36 and 36-72 hours reactivity (p < 0.0001 each), 36-72 hours normal background amplitude (p = 0.0004), and stimulus-induced discharges (p = 0.0001) correlated with favorable outcome. The combined 6-point score cutoff ≥2 was 100% sensitive (95% confidence interval [CI], 78%-100%) and 70% specific (95% CI, 59%-80%) for CPC 1-3 (area under the curve [AUC], 0.98; 95% CI, 0.94-1.00). Increasing score correlated with NSE (ρ = -0.46, p = 0.0001). In the validation cohort (41 patients), the score was 100% sensitive (95% CI, 60%-100%) and 88% specific (95% CI, 73%-97%) for CPC 1-3 (AUC, 0.96; 95% CI, 0.91-1.00). Prognostic value of early epileptiform EEG after CA can be estimated combining timing, continuity, reactivity, and amplitude features in a score that correlates with neuronal damage.

Chapter 8 - Child EEG (and maturation)

EEG changes during the perinatal period, infancy, childhood, and adolescence are concomitant with brain growth, myelination, expanding connectivity, and overall maturation, which are particularly fast during the first year of life. EEG aspects of early brain development are accessible in preterm during the third trimester of gestational age, and they evolve to full-term, infancy, and childhood EEG patterns. Each of these age periods shares specific EEG features that reach gross adult outlines in the first year. Interpreting EEG needs therefore a deep knowledge of pathological and normal EEG patterns with their variants belonging to each age range. Recording EEG during these periods also requires adapting the recording techniques to the specific age in order to obtain interpretable records. This chapter describes normal EEG features and variants, characteristic patterns of development, and some patterns that are unusual for age, from the neonatal period to adolescence.

Automatic Independent Component Scalp Map Analysis of Electroencephalogram During Motor Preparation.

This work presents a method for automatic independent component (IC) scalp map analysis of electroencephalogram during motor preparation in visuomotor tasks. The strength of this approach is the analysis of the IC scalp maps based on the apriori given mask. This uses an image processing approach, comparable to visual classification used by experts, to automate the selection of relevant ICs in visuomotor tasks. Thirty iterations of the Infomax ICA algorithm were used to test the reliability of the ICs. ICs above 95% quality index were used for IC scalp topography image analysis. Here, we used a linkage-clustering algorithm for IC clustering and gap statistic to estimate the number of clusters. After classifying the components with our approach, the labels were compared to those from well-known MARA ("Multiple Artifact Rejection Algorithm") - an open-source EEGLAB plug-in. It was found that 334 of the 568 labels were in-agreement. MARA labeled 81 out of the 177 source-related components, and 238 out of the 319 non-source-related components, as artifacts. Here, the strength of our approach lies in using an image-processing algorithm to identify the task-specific ICs whereas MARA focuses on the automatic classification of the artifactual ICs by combining stereotyped artifact-specific spatial and temporal features that depend on the electrode montage. After "artefactual" ICs are removed, task-specific ICs still remains to be identified from the remaining "good" ICs where our scalp topography image analysis approach can be applied. Our IC scalp topography image analysis is focused on task-specific IC selection based on an apriori mask, which is not limited to specific EEG features and/or electrode configurations for high-density EEG.

Electroencephalography Findings in Autoimmune and Neuroinflammation-Related Epilepsies

AbstractAutoimmune epilepsy occurs in association with localized central nervous system or systemic immune-mediated inflammation. Traditional anticonvulsant medications are frequently ineffective, and alternative approaches including immunomodulatory agents may be required. Identification of patients with autoimmune epilepsy may be challenging due to the variability of presentation and normal or nonspecific diagnostic results. Electroencephalography (EEG) features tend to include patterns of encephalopathy, generalized or focal interictal epileptiform abnormalities, or status epilepticus. Occasionally, specific EEG features may lend insight into the diagnosis. In this review article, we discuss EEG features of several suspected or proven immune-mediated pediatric epilepsies, including Rasmussen's encephalitis, idiopathic hemiconvulsion-hemiplegia syndrome, febrile infection-related epilepsy syndrome, epilepsies associated with autoantibodies to neuronal surface or intracellular antigens, and other systemic autoimmune diseases.

Methodological standards and interpretation of video-electroencephalography in adult control rodents. ATASK1-WG1 report of the AES/ILAE Translational TaskForce of the ILAE.

In vivo electrophysiological recordings are widely used in neuroscience research, and video-electroencephalography (vEEG) has become a mainstay of preclinical neuroscience research, including studies of epilepsy and cognition. Studies utilizing vEEG typically involve comparison of measurements obtained from different experimental groups, or from the same experimental group at different times, in which one set of measurements serves as "control" and the others as "test" of the variables of interest. Thus, controls provide mainly a reference measurement for the experimental test. Control rodents represent an undiagnosed population, and cannot be assumed to be "normal" in the sense of being "healthy." Certain physiological EEG patterns seen in humans are also seen in control rodents. However, interpretation of rodent vEEG studies relies on documented differences in frequency, morphology, type, location, behavioral state dependence, reactivity, and functional or structural correlates of specific EEG patterns and features between control and test groups. This paper will focus on the vEEG of standard laboratory rodent strains with the aim of developing a small set of practical guidelines that can assist researchers in the design, reporting, and interpretation of future vEEG studies. To this end, we will: (1) discuss advantages and pitfalls of common vEEG techniques in rodents and propose a set of recommended practices and (2) present EEG patterns and associated behaviors recorded from adult rats of a variety of strains. We will describe the defining features of selected vEEG patterns (brain-generated or artifactual) and note similarities to vEEG patterns seen in adult humans. We will note similarities to normal variants or pathological human EEG patterns and defer their interpretation to a future report focusing on rodent seizure patterns.

S170 REM – Dreaming – Emotions: The neurophysiology of nightmare disorder

Objectives Nightmares are intense and highly unpleasant mental experiences that occur usually – but not exclusively – during late-night Rapid Eye Movement (REM) sleep and often provoke abrupt awakenings.The underlying mechanism of nightmare disorder was only scarcely investigated and remains poorly understood. The most influential model of nightmare disorder that provided a common framework fortrauma-related and non-traumatic (idiopathic) nightmares was introduced by Levin and Nielsen (2007). One of the main assumptions of the model is that nightmares reflect unsuccessful fear-extinction processes during (REM) sleep due to a dysfunctional brain network comprising fronto-limbic structures. Consequently, amygdalar over-activation and inefficient (prefrontal) inhibition lead to the emotional intensification of dreaming and hyperarousal during sleep. Nevertheless, sleep quality and emotional regulation in nightmare sufferers was mainly examined by questionnaire-based studies. Methods We have performed a series of quantitative EEG analyses based on the polysomnographic data of young (non-ptsd) nightmare sufferers and matched controls. Moreover, in a subsequent study we investigated emotional habituation to arousing images after a night of polysomnographically monitored sleep in a group of nightmare sufferers and matched controls. Emotional reactivity was quantified by subjective evaluations and psychophysiological measures (HRV, SCR). Results In line with questionnaire-based findings, we found that nightmare sufferers are characterized by fragmented sleep, reduced Slow Wave Sleep (SWS), enhanced microarousals during NREM sleep and heightened wake-like oscillations during REM sleep. Furthermore, arousal-related cortical activity coupled with attenuated parasympathetic regulation were evidenced in nightmare sufferers specifically during NREM to REM transitions. Discussion Given that the neural network underlying emotional memory processing is highly active during REM periods, and REM sleep seems to be intimately related to fear memory processing, our findings might indirectly support Levin and Nielsen’s assumption of disrupted REM-dependent fear-extinction in nightmare disorder. Nevertheless, the association between impaired emotional regulation and altered sleep physiology remains to be investigated. Conclusions In our experiments we aimed to directly test Levin and Nielsen’s fear-extintion model of nightmare disorder and to relate impaired emotional regulation to specific sleep EEG features. We conclude that examining nightmare disorder by such integrative approach would shed more light on the enigma of REM sleep and emotional memory processing. Significance Our research could shed more light on the mechanism of nightmare formation, propagate the professional knowledge of nightmare disorder, and facilitate the development of effective, evidence-based treatment procedures.

Interrater agreement in the interpretation of neonatal electroencephalography in hypoxic-ischemic encephalopathy.

Research using neonatal electroencephalography (EEG) has been limited by a lack of a standardized classification system and interpretation terminology. In 2013, the American Clinical Neurophysiology Society (ACNS) published a guideline for standardized terminology and categorization in the description of continuous EEG in neonates. We sought to assess interrater agreement for this neonatal EEG categorization system as applied by a group of pediatric neurophysiologists. A total of 60 neonatal EEG studies were collected from three institutions. All EEG segments were from term neonates with hypoxic-ischemic encephalopathy. Three pediatric neurophysiologists independently reviewed each record using the ACNS standardized scoring system. Unweighted kappa values were calculated for interrater agreement of categorical data across multiple observers. Interrater agreement was very good for identification of seizures (κ=0.93, p<0.001), with perfect agreement in 95% of records (57 of 60). Interrater agreement was moderate for classifying records as normal or having any abnormality (κ=0.49, p<0.001), with perfect agreement in 78% of records (47 of 60). Interrater agreement was good in classifying EEG backgrounds on a 5-category scale (normal, excessively discontinuous, burst suppression, status epilepticus, or electrocerebral inactivity) (κ=0.70, p<0.001), with perfect agreement in 72% of records (43 of 60). Other specific background features had lower agreement, including voltage (κ=0.41, p<0.001), variability (κ=0.35, p<0.001), symmetry (κ=0.18, p=0.01), presence of abnormal sharp waves (κ<0.20, p<0.05), and presence of brief rhythmic discharges (κ<0.20, p<0.05). We found good or very good interrater agreement applying the ACNS system for identification of seizures and classification of EEG background. Other specific EEG features showed limited interrater agreement. Of importance to both clinicians and researchers, our findings support using the ACNS system in identifying seizures and classifying backgrounds of neonatal EEG recordings, but also suggest limited reproducibility for certain other EEG features.

Benefits of Instructed Responding in Manual Assembly Tasks: An ERP Approach.

The majority of neuroergonomics studies are focused mainly on investigating the interaction between operators and automated systems. Far less attention has been dedicated to the investigation of brain processes in more traditional workplaces, such as manual assembly, which are still ubiquitous in industry. The present study investigates whether assembly workers’ attention can be enhanced if they are instructed with which hand to initiate the assembly operation, as opposed to the case when they can commence the operation with whichever hand they prefer. For this aim, we replicated a specific workplace, where 17 participants in the study simulated a manual assembly operation of the rubber hoses that are used in vehicle hydraulic brake systems, while wearing wireless electroencephalography (EEG). The specific EEG feature of interest for this study was the P300 components’ amplitude of the event-related potential (ERP), as it has previously been shown that it is positively related to human attention. The behavioral attention-related modality of reaction times (RTs) was also recorded. Participants were presented with two distinct tasks during the simulated operation, which were counterbalanced across participants. In the first task, digits were used as indicators for the operation initiation (Numbers task), where participants could freely choose with which hand they would commence the action upon seeing the digit. In the second task, participants were presented with arrows, which served as instructed operation initiators (Arrows task), and they were instructed to start each operation with the hand that corresponded to the arrow direction. The results of this study showed that the P300 amplitude was significantly higher in the instructed condition. Interestingly, the RTs did not differ across any task conditions. This, together with the other findings of this study, suggests that attention levels can be increased using instructed responses without compromising work performance or operators’ well-being, paving the way for future applications in manual assembly task design.

Risk of seizures in children with tectal gliomas.

The objective of this study was to determine the prevalence of seizures in children with tectal gliomas and to determine if there are common clinical, electroencephalography (EEG), or radiologic findings that predict risk of seizures in these patients. We conducted a retrospective review of all patients with tectal gliomas over a 22-year period at a single institution. Data extraction included sex, age at presentation of tectal glioma and age of presentation with seizures, magnetic resonance imaging (MRI) findings, seizure frequency and semiology, and EEG findings. We identified 79 patients, 66 of whom had adequate imaging and clinical data for further analysis. Eight patients (12.1%) had a history of seizures. Three patients had a clear symptomatic cause of seizures. Three patients were diagnosed with a tectal glioma as an incidental finding after a first seizure. One patient had a history of febrile convulsions. One patient had a generalized seizure 5 years after presenting with macrocephaly. Although the risk of seizure in children with known tectal glioma was relatively high, we did not identify specific clinical, radiologic, EEG, or MRI features that are predictive of increased risk. Thus, in children with tectal gliomas who have seizures, alternative causes for the seizures must be sought.