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We describe the case of a woman in her 60s admitted to the intensive care unit after a first generalised tonic-clonic seizure in the context of alcohol withdrawal. She was placed under invasive mechanical ventilation due to persistence of coma despite antiepileptic treatment. Despite continuous sedation with propofol, the frequency and intensity of seizure increased. Seizures were very similar to epileptic tonic-clonic seizures and were recorded with video and electroencephalogram (EEG). A diagnosis of tetanus was considered after a scalp wound was discovered. The patient’s husband revealed that a trismus had appeared a few days before hospital admission after a head trauma. EEG showed a pattern of diffuse spikes, which disappeared after a cisatracurium bolus. The diagnosis of tetanus was later confirmed by cultures from wound samples. Therefore, severe tetanus can mimic both the clinical and EEG features of status epilepticus and could be added to the differential diagnosis of epilepsy.

Alzheimer’s disease (AD) is a neurodegenerative disorder known to affect functional connectivity (FC) across many brain regions. Linear FC measures have been applied to study the differences in AD by splitting neurophysiological signals, such as electroencephalography (EEG) recordings, into discrete frequency bands and analysing them in isolation from each other. We address this limitation by quantifying cross-frequency FC in addition to the traditional within-band approach. Cross-bispectrum, a higher-order spectral analysis approach, is used to measure the nonlinear FC and is compared with the cross-spectrum, which only measures the linear FC within bands. This work reports the reconstruction of a cross-frequency FC network where each frequency band is treated as a layer in a multilayer network with both inter- and intra-layer edges. Cross-bispectrum detects cross-frequency differences, mainly increased FC in AD cases inδ-θcoupling. Overall, increased strength of low-frequency coupling and decreased level of high-frequency coupling is observed in AD cases in comparison to healthy controls (HC). We demonstrate that a graph-theoretic analysis of cross-frequency brain networks is crucial to obtain a more detailed insight into their structure and function. Vulnerability analysis reveals that the integration and segregation properties of networks are enabled by different frequency couplings in AD networks compared to HCs. Finally, we use the reconstructed networks for classification. The extra cross-frequency coupling information can improve the classification performance significantly, suggesting an important role of cross-frequency FC. The results highlight the importance of studying nonlinearity and including cross-frequency FC in characterising AD.

Neuronal ceroid lipofuscinosis is a rare childhood neurodegenerative disease, classified under the spectrum of progressive myoclonic epilepsy (PME). Cognitive decline, seizures including myoclonus, vision loss and ataxia are the commonly described manifestations of this illness. While visual failure in this disease is largely attributed to retinal, macular degeneration and optic atrophy, with this index case, we report an atypical presentation in the form of higher order visual dysfunction. The pattern of cognitive regression has further been explored here with higher order visual dysfunction and language regression being the predominant manifestations, stemming from an involvement of bilateral occipitoparietal/occipitotemporal networks. Yet another unique feature of this case also lies in the occurrence of myoclonic-atonic seizure, a semiology rarely reported before in PME.

The mechanisms behind Alzheimer's disease are not yet fully described, and changes in the electrophysiology of patients across the continuum of the disease could help to understand them. In this work, we study the power spectral distribution of a set of 129 individuals from the Connectomics of Brian Aging and Dementia project. From this sample, we acquired task-free data, with eyes closed, and estimated the power spectral distribution in source space. We compared the spectral profiles of three groups of individuals: 70 healthy controls, 27 patients with amnestic MCI, and 32 individuals showing cognitive impairment without subjective complaints (IWOC). The results showed a slowing of the brain activity in the aMCI patients, when compared to both the healthy controls and the IWOC individuals. These differences appeared both as a decrease in power for high frequency oscillations and an increase in power in alpha oscillations. The slowing of the spectrum was significant mainly in parietal and medial frontal areas. We were able to validate the slowing of the brain activity in individuals with aMCI, appearing in our sample in areas related to the default mode network. However, this pattern did not appear in the IWOC individuals, suggesting that their condition is not part of the AD continuum. This work raises interesting questions about this group of individuals, and the underlying brain mechanisms behind their cognitive impairment.

Chronic cannabis use is maintained in part through dysregulated stress and reward response systems. Specifically, stress-related negative affect is thought to act as a salient motivator for chronic substance use. Models of addiction posit that the transition from positive to negative reinforcement motives for substance use is a key mechanism of disordered use. However, research in substance-using samples has not assessed stress-related neural processing of both positive and negative reinforcement.Therefore, the present study utilized laboratory stress induction to examine how stress affects the reward positivity, an event-related potential sensitive to both positive (RewP) and negative (relief-RewP) reinforcement, in 87 cannabis users (58.10% female,Findings indicated that CUD severity was not associated with RewP/relief-RewP amplitude regardless of acute stress. Exploratory analyses revealed, however, that among those with history of TBI +, CUD severity was associated with greater stress-elicited blunting of the RewP and enhancement of the relief-RewP.Although initial findings contradict current allostatic models of addiction, exploratory findings suggest that history of TBI, and potentially other confounding variables related to increased risk of TBI experience, may influence the extent to which stressful experiences modulate the neurophysiology of both positive and negative reinforcement reward processing in CUD. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Landau-Kleffner syndrome (LKS) is described by the International Classification of Epileptic Syndromes since 1985 as a constellation of clinical and electrographic signs, including acquired aphasia, regression of language milestones and seizures, along with sleep-activated paroxysms on electroencephalogram which can progress to electrographic status epilepticus of sleep. In this case, a 7-year-old boy presented with an atypical history of new-onset aphasia and regression of language milestones with rare seizures. However, there was an electrographic mismatch in the form of right-sided epileptiform activity and continuous spike and wave of sleep pattern. Detailed speech analysis and perusal of the history revealed a possibly ambidextrous child with right hemispheric language dominance, and he was diagnosed with LKS and treated. This report illustrates the many pitfalls in the diagnosis and treatment of this rare epileptic syndrome.

Electroencephalogram (EEG) discontinuity can occur at high concentrations of anesthetic drugs, reflecting suppression of electrocortical activity. This EEG pattern has been reported in children and reflects a deep state of anesthesia. Isoelectric events on the EEG, a more extreme degree of voltage suppression, have been shown to be associated with worse long-term neurologic outcomes in neonates undergoing cardiac surgery. However, the clinical significance of EEG discontinuities during pediatric anesthesia for noncardiac surgery is not yet known and merits further research. In this study, we assessed the incidence of EEG discontinuity during anesthesia induction in neurologically normal infants and the clinical factors associated with its development. We hypothesized that EEG discontinuity would be associated with sevoflurane-induced alpha (8-12 Hz) power during the period of anesthesia induction in infants.We prospectively recorded 26 channels of EEG during anesthesia induction in an observational cohort of 54 infants (median age, 7.6 months; interquartile range [IQR] [4.9-9.8 months]). We identified EEG discontinuity, defined as voltage amplitude25 microvolts for2 seconds, and assessed its association with sevoflurane-induced alpha power using spectral analysis and multivariable logistic regression adjusting for clinically important variables.EEG discontinuity was observed in 20 of 54 subjects (37%), with a total of 25 discrete events. Sevoflurane-induced alpha power in the posterior regions of the head (eg, parietal or occipital regions) was significantly lower in the EEG discontinuity group (midline parietal channel on the electroencephalogram, International 10-20 System [Pz]; 8.3 vs 11.2 decibels [dBs]; P = .004), and this association remained after multivariable adjustment (adjusted odds ratio [aOR] = 0.51 per dB increase in alpha power [95% CI, 0.30-0.89]; P = .02). There were no differences in the baseline (unanesthetized) EEG between groups in alpha power or power in any other frequency band.We demonstrate that EEG discontinuity is common during anesthesia induction and is related to the level of sevoflurane-induced posterior alpha power, a putative marker of cortical-thalamic circuit development in the first year of life. This association persisted even after adjusting for age and propofol coadministration. The fact that this difference was only observed during anesthesia and not in the baseline EEG suggests that otherwise hidden brain circuit properties are unmasked by general anesthesia. These neurophysiologic markers observed during anesthesia may be useful in identifying patients who may have a greater chance of developing discontinuity.

Background Coma is one of the frequently encountered clinical conditions in any intensive care unit (ICU), which is responsible for considerable morbidity and mortality. Therefore, this study was designed to look at the clinical and EEG profile of Nonconvulsive Status Epilepticus (NCSE) in comatose ICU patients using portable electroencephalography (EEG). Methods In all 102 patients of unresponsive coma (GCS ≤ 8), who remained in poor sensorium despite 48 h of optimum treatment in ICU, were included in the study. All patients underwent 1 h of electroencephalography (EEG) monitoring with a portable EEG machine. All EEGs were screened according to Salzburg Consensus Criteria (SCC) for Nonconvulsive Status Epilepticus (NCSE). Patients with evidence of NCSE were administered parenteral Antiepileptic Drugs (AED). A repeat EEG was done after 24 h of baseline to ascertain the effect of AED. The primary outcome was the recognition of patients with NCSE on the basis of established EEG criteria. The secondary outcome measure was the Glasgow outcome scale (GOS) at the time of discharge. Results Out of 102 cases enrolled, 12 (11.8%) cases were detected to have NCSE on portable EEG. The mean age of patients with NCSE was 52.2 years. In terms of gender distribution, 2/12 (17%) were female, and 10/12 (83%) were male (M: F = 5:1). Median GCS was 6 (range 3–8). Looking at CNS infections, 4/12 (33.3%) had evidence of some form of CNS infection in the NCSE group, compared to 16/90 (18%) in the group without NCSE. This difference was statistically significant (P-value 2 points) after administration of AED’ was noted with good clinical outcomes (GOS 5). In five of these 12 cases, death was the final outcome (GOS 1). Conclusions NSCE should be considered in the differential diagnosis of all unresponsive comatose ICU patients. In resource-limited settings, where continuous EEG monitoring may not be feasible, bedside portable EEG testing can be used to diagnose patients with NCSE. Treating NCSE reverses epileptiform EEG changes and improves clinical outcomes in a subset of comatose ICU patients.

Introduction: The right and left-hand motor imagery (MI) analysis based on the electroencephalogram (EEG) signal can directly link the central nervous system to a computer or a device. This study aims to identify a set of robust and nonlinear effective brain connectivity features quantified by transfer entropy (TE) to characterize the relationship between brain regions from EEG signals and create a hierarchical feature selection and classification for discrimination of right and lefthand MI tasks. Methods: TE is calculated among EEG channels as the distinctive, effective connectivity features. TE is a model-free method that can measure nonlinear effective connectivity and analyze multivariate dependent directed information flow among neural EEG channels. Then four feature subset selection methods namely relief-F, Fisher, Laplacian, and local learningbased clustering (LLCFS) algorithms are used to choose the most significant effective connectivity features and reduce redundant information. Finally, support vector machine (SVM) and linear discriminant analysis (LDA) methods are used for classification. Results: Results show that the best performance in 29 healthy subjects and 60 trials is achieved using the TE method via the Relief-F algorithm as feature selection and support vector machine (SVM) classification with 91.02% accuracy. Conclusion: The TE index and a hierarchical feature selection and classification can be useful for the discrimination of right- and left-hand MI tasks from multichannel EEG signals.

Funding Information: JCHP and RJI want to dedicate this work to the memory of Prof. Jukka Sarvas, one of the most beautiful minds and humble hearts that have existed in this world. HRS holds a 5-year professorship in precision medicine at the Faculty of Health Sciences and Medicine, University of Copenhagen which is sponsored by the Lundbeck Foundation (Grant Nr. R186-2015-2138). TPM has been supported by the Academy of Finland (Grant No. 321631 ). Funding Information: The Brain Electrophysiological recording and STimulation (BEST) toolbox (www.best-toolbox.org) is an open-source MATLAB toolbox with GUI [216], which enables the user to easily design, save/load, run, and online analyze multi-protocol/multi-session experiments involving a variety of brain stimulation techniques, such as TMS, TES and also transcranial ultrasound stimulation. It interfaces with many recording and stimulation devices and can online analyze and display the input signals from EMG and EEG and change TMS parameters on the fly (via the MAGIC toolbox, [246]), thereby facilitating real-time applications. Besides several modules for conducting MEP measurements of all kinds (such as motor hotspot search, threshold hunting, MEP measurements, dose-response curves, as well as paired-pulse and double-coil protocols), the BEST toolbox also supports TEP hotspot search and TEP measurements by providing online graphical feedback for re-referenced EEG signals (also lead fields for arbitrary spatial filters can be defined), incremental condition-wise time-locked TEP averages and topographical maps of selected TEP components. Future releases are planned to also provide real-time artifact rejection methods. The BEST toolbox does not provide a built-in TMS–EEG artifact correction pipeline but can interact with all MATLAB-based pipelines or toolboxes. The internal data format is based on FieldTrip.The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: PJ has received consulting fees and shares a patent with Nexstim Oyj. PL has received consulting fees from Nexstim Oyj. HRS has received honoraria as speaker from Sanofi Genzyme, Denmark, Lundbeck AS, Denmark, and Novartis, Denmark, as consultant from Sanofi Genzyme, Denmark, Lophora, Denmark, and Lundbeck AS, Denmark, and as editor-in-chief (Neuroimage Clinical) and senior editor (NeuroImage) from Elsevier Publishers, Amsterdam, The Netherlands. He has received royalties as book editor from Springer Publishers, Stuttgart, Germany and from Gyldendal Publishers, Copenhagen, Denmark. TPM has successfully applied for funding for a collaborative research project (project not started at the time of the submission) with Bittium Biosignals Oy (Kuopio, Finland). SC is advisor and share-holder of Intrinsic Power, a spinf-off of the University of Milan.JCHP and RJI want to dedicate this work to the memory of Prof. Jukka Sarvas, one of the most beautiful minds and humble hearts that have existed in this world. HRS holds a 5-year professorship in precision medicine at the Faculty of Health Sciences and Medicine, University of Copenhagen which is sponsored by the Lundbeck Foundation (Grant Nr. R186-2015-2138). TPM has been supported by the Academy of Finland (Grant No. 321631). Publisher Copyright: © 2023 The Authors Transcranial magnetic stimulation (TMS) evokes neuronal activity in the targeted cortex and connected brain regions. The evoked brain response can be measured with electroencephalography (EEG). TMS combined with simultaneous EEG (TMS−EEG) is widely used for studying cortical reactivity and connectivity at high spatiotemporal resolution. Methodologically, the combination of TMS with EEG is challenging, and there are many open questions in the field. Different TMS−EEG equipment and approaches for data collection and analysis are used. The lack of standardization may affect reproducibility and limit the comparability of results produced in different research laboratories. In addition, there is controversy about the extent to which auditory and somatosensory inputs contribute to transcranially evoked EEG. This review provides a guide for researchers who wish to use TMS−EEG to study the reactivity of the human cortex. A worldwide panel of experts working on TMS−EEG covered all aspects that should be considered in TMS−EEG experiments, providing methodological recommendations (when possible) for effective TMS−EEG recordings and analysis. The panel identified and discussed the challenges of the technique, particularly regarding recording procedures, artifact correction, analysis, and interpretation of the transcranial evoked potentials (TEPs). Therefore, this work offers an extensive overview of TMS−EEG methodology and thus may promote standardization of experimental and computational procedures across groups.

The present study investigates the effectiveness of a deep learning neural network for non-invasively localizing the seizure onset zone (SOZ) using multi-modal MRI data that are clinically acquired from children with drug-resistant epilepsy. A cortical parcellation was applied to localize the SOZ in cortical nodes of the epileptogenic hemisphere. At each node, the laminar surface analysis was followed to sample 1) the relative intensity of gray matter and white matter in multi-modal MRI and 2) the neighboring white matter connectivity using diffusion tractography edge strengths. A cross-validation was employed to train and test all layers of a multi-scale residual neural network (msResNet) that can classify SOZ node in an end-to-end fashion. A prediction probability of a given node belonging to the SOZ class was proposed as a non-invasive MRI marker of seizure onset likelihood. In an independent validation cohort, the proposed MRI marker provided a very large effect size of Cohen's d = 1.21 between SOZ and non-SOZ, and classified SOZ with a balanced accuracy of 0.75 in lesional and 0.67 in non-lesional MRI groups. The subsequent multi-variate logistic regression found the incorporation of the proposed MRI marker into interictal intracranial EEG (iEEG) markers further improves the differentiation between the epileptogenic focus (defined as SOZ resected during surgery) and non-epileptogenic sites (i.e., non-SOZ sites preserved during surgery) up to 15 % in non-lesional MRI group, suggesting that the proposed MRI marker could improve the localization of epileptogenic foci for successful pediatric epilepsy surgery.

A growing body of literature suggests that cognitive impairment in people with schizophrenia (PSZ) results from disrupted cortical excitatory/inhibitory (E-I) balance, which may be linked to gamma entrainment and can be measured noninvasively using electroencephalography (EEG). However, it is not yet known the degree to which these entrainment abnormalities covary within subjects across sensory modalities. Furthermore, the degree to which cross-modal gamma entrainment reflects variation in biological processes associated with cognitive performance remains unclear. We used EEG to measure entrainment to repetitive auditory and visual stimulation at beta (20 Hz) and gamma (30 and 40 Hz) frequencies in PSZ (n = 78) and healthy control subjects (HCS; n = 80). Three indices were measured for each frequency and modality: event-related spectral perturbation (ERSP), intertrial coherence (ITC), and phase-lag angle (PLA). Cognition and symptom severity were also assessed. We found little evidence that gamma entrainment covaried across sensory modalities. PSZ exhibited a modest correlation between modalities at 40 Hz for ERSP and ITC measures (r = 0.23-0.24); however, no other significant correlations between modalities emerged for either HCS or PSZ. Both univariate and multivariate analyses revealed that (a) the pattern of entrainment abnormalities in PSZ differed across modalities, and (b) modality rather than frequency band was the main source of variance. Finally, we observed a significant association between cognition and gamma entrainment in the auditory domain only in HCS. Gamma-band EEG entrainment does not reflect a unitary transcortical mechanism but is instead modality specific. To the extent that entrainment reflects the integrity of cortical E-I balance, the deficits observed in PSZ appear to be modality specific and not consistently associated with cognitive impairment. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Spike-wave discharges (SWDs) are among the most prominent electrical signals recordable from the rat cerebrum. Increased by inbreeding, SWDs have served as an animal model of human genetic absence seizures. Yet, SWDs are ubiquitous in inbred and outbred rats, suggesting they reflect normal brain function. We hypothesized that SWDs represent oscillatory neural ensemble activity underlying sensory encoding. To test this hypothesis, we simultaneously mapped SWDs from wide areas (8 × 8 mm) of both hemispheres in anesthetized rats, using 256-electrode epicortical arrays that covered primary and secondary somatosensory, auditory and visual cortex bilaterally. We also recorded the laminar pattern of SWDs with linear microelectrode arrays. We compared the spatial and temporal organization of SWDs to somatosensory-evoked potentials (SEPs), as well as auditory- and visual-evoked potentials (AEPs and VEPs) to examine similarities and/or differences between sensory-evoked and spontaneous oscillations in the same animals. We discovered that SWDs are confined to the facial representation of primary and secondary somatosensory cortex (SI and SII, respectively), areas that are preferentially engaged during environmental exploration in the rat. Furthermore, these oscillations exhibit highly synchronized bilateral traveling waves in SI and SII, simultaneously forming closely matched spread patterns in both hemispheres. We propose that SWDs could reflect a previously unappreciated capacity for rat somatosensory cortex to perform precise spatial and temporal analysis of rapidly changing sensory input at the level of large neural ensembles synchronized both within and between the cerebral hemispheres.

There is growing interest in developing training programs to mitigate cognitive decline associated with normal aging. Here, we assessed the effect of 3-month music and visual art training programs on the oscillatory brain activity of older adults using a partially randomized intervention design. High-density electroencephalography (EEG) was measured during the pre- and post-training sessions while participants completed a visual GoNoGo task. Time-frequency representations were calculated in regions of interest encompassing the visual, parietal, and prefrontal cortices. Before training, NoGo trials generated greater theta power than Go trials from 300 to 500 ms post-stimulus in mid-central and frontal brain areas. Theta power indexing response suppression was significantly reduced after music training. There was no significant difference between pre- and post-test for the visual art or the control group. The effect of music training on theta power indexing response suppression was associated with reduced functional connectivity between prefrontal, visual, and auditory regions. These results suggest that theta power indexes executive control mechanisms in older adults. Music training affects theta power and functional connectivity associated with response suppression. These findings contribute to a better understanding of inhibitory control ability in older adults and the neuroplastic effects of music interventions.

Introduction The authors tested the hypothesis that the EEG feature generalized polyspike train (GPT) is associated with drug-resistant idiopathic generalized epilepsy (IGE). Methods The authors conducted a single-center case-control study of patients with IGE who had outpatient EEGs performed between 2016 and 2020. The authors classified patients as drug-resistant or drug-responsive based on clinical review and in a masked manner reviewed EEG data for the presence and timing of GPT (a burst of generalized rhythmic spikes lasting less than 1 second) and other EEG features. A relationship between GPT and drug resistance was tested before and after controlling for EEG duration. The EEG duration needed to observe GPT was also calculated. Results One hundred three patients were included (70% drug-responsive and 30% drug-resistant patients). Generalized polyspike train was more prevalent in drug-resistant IGE (odds ratio, 3.8; 95% confidence interval, 1.3-11.4; P = 0.02). This finding persisted when controlling for EEG duration both with stratification and with survival analysis. A median of 6.5 hours (interquartile range, 0.5-12.7 hours) of EEG recording was required to capture the first occurrence of GPT. Conclusions The findings support the hypothesis that GPT is associated with drug-resistant IGE. Prolonged EEG recording is required to identify this feature. Thus, >24-hour EEG recording early in the evaluation of patients with IGE may facilitate prognostication.

Correctly diagnosing and classifying seizures and epilepsies is paramount to ensure the delivery of optimal care to patients with epilepsy. Focal seizures, defined as those that originate within networks limited to one hemisphere, are primarily subdivided into focal aware, focal impaired awareness, and focal to bilateral tonic–clonic seizures. Focal epilepsies account for most epilepsy cases both in children and adults. In children, focal epilepsies are typically subdivided in three groups: self-limited focal epilepsy syndromes (e.g., self-limited epilepsy with centrotemporal spikes), focal epilepsy of unknown cause but which do not meet criteria for a self-limited focal epilepsy syndrome, and focal epilepsy of known cause (e.g., structural lesions—developmental or acquired). In adults, focal epilepsies are often acquired and may be caused by a structural lesion such as stroke, infection and traumatic brain injury, or brain tumors, vascular malformations, metabolic disorders, autoimmune, and/or genetic causes. In addition to seizure semiology, neuroimaging, neurophysiology, and neuropathology constitute the cornerstones of a diagnostic evaluation. Patients with focal epilepsy who become drug-resistant should promptly undergo assessment in an epilepsy center. After excluding pseudo-resistance, these patients should be considered for presurgical evaluation as a means to identify the location and extent of the epileptogenic zone and assess their candidacy for a surgical procedure. The goal of this seminar in epileptology is to summarize clinically relevant information concerning focal epilepsies. This contributes to the ILAE's mission to ensure that worldwide healthcare professionals, patients, and caregivers continue to have access to high-quality educational resources concerning epilepsy.

While previous research has indicated that alcohol use is associated with difficulties in emotion processing and socioemotional functioning, less is known about the effects of cannabis on these functions. The purpose of this review article is to provide the current state of knowledge on the effects of cannabis on emotion processing with regard to behavioral, physiological, and neural responses. This narrative review synthesizes previous research investigating the effects of cannabis on emotion processing across studies that have utilized a number of experimental approaches to determine both the acute and chronic effects of cannabis on emotion processing. Limitations of current research and steps for future directions are discussed. Existing research has shown that cannabis use is associated with difficulties in emotion processing, such as impairments in correctly identifying emotions and problems with emotion differentiation. Electroencephalography (EEG) studies have produced mixed findings, but have considered a number of variables, such as participant sex, and comorbid depression. In addition, while there are mixed findings for the effects of cannabis on amygdalar brain activity across functional magnetic resonance imaging studies, several studies indicate that cannabis use is linked with decreased brain response in the frontal lobe while viewing emotional stimuli. To our knowledge, this is one of the first critical review articles focused on an emerging research area of cannabis and emotion processing. Synthesizing the existing findings in this developing research field is important for future prevention and intervention studies focused on promoting healthy socioemotional functioning in cannabis users. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Neurophysiological studies exploring involuntary attention have reported that electroencephalographic (EEG) measures can indicate impaired neural processing from initial stages of Parkinson's disease (PD). Since involuntary attention is regulated by right hemisphere networks and PD generally initiates its motor symptomatology unilaterally, whether involuntary attention is impaired depending on the onset side of PD remains unknown.We compared the neurophysiological correlates of involuntary attention among a PD group with left-side onset (L-PD), a PD group with right-side onset (R-PD) symptomatology, and a healthy control group (HC). All participants performed an auditory involuntary attention task while a digital EEG was recorded.Our main finding was a reduction both in the P3a amplitude and evoked delta-theta phase alignment in the L-PD group compared to the HC. Further, there was a significant correlation between P3a amplitude and disease duration in the R-PD, but not in the L-PD group. Behaviorally, both clinical groups, and in particular L-PD, showed reduced orientation towards novel stimuli, and no reduction of distraction effects during the experiment.Our results indicate that involuntary attention is differentially impaired in patients with left side onset of symptoms. Involuntary attention impairment might be present from initial stages of left onset PD and become progressively compromised in patients with right onset PD.The onset side of symptomatology should be considered for attentional impairment in PD.

Emotion recognition from EEG signals has attracted much attention in affective computing. Recently, a novel dynamic graph convolutional neural network (DGCNN) model was proposed, which simultaneously optimized the network parameters and a weighted graph G characterizing the strength of functional relation between each pair of two electrodes in the EEG recording equipment. In this paper, we propose a sparse DGCNN model which improves the DGCNN by imposing a sparseness constraint on G. Our work is based on an important observation: the tomography study reveals that different brain regions sampled by EEG electrodes may be related to different functions of the brain and then the functional relations among electrodes are possibly highly localized and sparse. However, introducing sparseness constraint into the graph G makes the loss function of sparse DGCNN non-differentiable at some singular points. To ensure that the training process of sparse DGCNN converges, we apply the forward-backward splitting method. To evaluate the performance of sparse DGCNN, we compare it with four representative recognition methods as well as different features and spectral bands. The results show that (1) sparse DGCNN has consistently better accuracy than representative methods and has a good scalability, and (2) DE, PSD and ASM features on γ bands convey most discriminative emotional information, and fusion of separate features and frequency bands can improve recognition performance.

Counselors are obligated to work effectively with diverse groups, yet stereotyping and biases can impede counselors’ efforts. Understanding implicit processes in social cognition among counselors is important because implicit stereotyping and biases can negatively influence counselors’ work. Investigation of cognitive processes through electroencephalography (EEG) can illuminate implicit tendencies that potentially lead to microaggressions toward clients. In this study, we tested the hypothesis that graduate students in mental health fields would show indicators of stereotyping in a priming experiment that measured stereotyping implicitly. Both neural (N400 event-related potential component) and behavioral (reaction time) measures were used as indexes for stereotyping. The results showed neural and behavioral markers of stereotyping toward Black men and White women in a sample of graduate students in mental health fields composed of mostly White women. Implications for counseling practice and research are discussed.

Epileptiform activity is common in critically ill patients, but movement-related artifacts-including electromyography (EMG) and myoclonus-can obscure EEG, limiting detection of epileptiform activity. We sought to determine the ability of pharmacologic paralysis and quantitative artifact reduction (AR) to improve epileptiform discharge detection.Retrospective analysis of patients who underwent continuous EEG monitoring with pharmacologic paralysis. Four reviewers read each patient's EEG pre- and post- both paralysis and AR, and indicated the presence of epileptiform discharges. We compared the interrater reliability (IRR) of identifying discharges at baseline, post-AR, and post-paralysis, and compared the performance of AR and paralysis according to artifact type.IRR of identifying epileptiform discharges at baseline was slight (N = 30; κ = 0.10) with a trend toward increase post-AR (κ = 0.26, p = 0.053) and a significant increase post-paralysis (κ = 0.51, p = 0.001). AR was as effective as paralysis at improving IRR of identifying discharges in those with high EMG artifact (N = 15; post-AR κ = 0.63, p = 0.009; post-paralysis κ = 0.62, p = 0.006) but not with primarily myoclonus artifact (N = 15).Paralysis improves detection of epileptiform activity in critically ill patients when movement-related artifact obscures EEG features. AR improves detection as much as paralysis when EMG artifact is high, but is ineffective when the primary source of artifact is myoclonus.In the appropriate setting, both AR and paralysis facilitate identification of epileptiform activity in critically ill patients.

Epileptic diathesis is an inherited neurophysiological trait that contributes to the development of all types of epilepsy. The amount of resting-state electroencephalography (EEG) theta activity is proportional to the degree of cortical excitability and epileptic diathesis. Our aim was to explore the amount and topographic distribution of theta activity in epilepsy groups. We hypothesized that the anatomical distribution of increased theta activity is independent of the epilepsy type.Patients with unmedicated idiopathic generalized epilepsy (IGE, n = 92) or focal epilepsy (FE, n = 149) and non-seizure patients with mild to moderate cerebral lesions (NONEP, n = 99) were compared to healthy controls (NC, n = 114). We analysed artifact-free EEG activity and defined multiple distributed sources of theta activity in the source space via low resolution electromagnetic tomography software. Age-corrected and Z-transformed theta values were compared across the groups.The rank of increased theta activity was IGE FE NONEP NC. Both epilepsy groups showed significantly more theta activity than did the NC group. Maximum theta abnormality occurred in the medial-basal prefrontal and anterior temporal cortex in both epilepsy groups.We confirmed the hypothesis outlined above.The common topographical pattern of increased EEG theta activity is correlated with epileptic diathesis, regardless of the epilepsy type.

Introduction: Nowadays, deep learning and convolutional neural networks (CNNs) have become widespread tools in many biomedical engineering studies. CNN is an end-to-end tool, which makes the processing procedure integrated, but in some situations, this processing tool requires to be fused with machine learning methods to be more accurate. Methods: In this paper, a hybrid approach based on deep features extracted from wavelet CNNs (WCNNs) weighted layers and multiclass support vector machine (MSVM) was proposed to improve the recognition of emotional states from electroencephalogram (EEG) signals. First, EEG signals were preprocessed and converted to Time-Frequency (T-F) color representation or scalogram using the continuous wavelet transform (CWT) method. Then, scalograms were fed into four popular pre-trained CNNs, AlexNet, ResNet-18, VGG-19, and Inception-v3 to fine-tune them. Then, the best feature layer from each one was used as input to the MSVM method to classify four quarters of the valence-arousal model. Finally, the subject-independent leave-one-subject-out criterion was used to evaluate the proposed method on DEAP and MAHNOB-HCI databases. Results: Results showed that extracting deep features from the earlier convolutional layer of ResNet-18 (Res2a) and classifying using the MSVM increased the average accuracy, precision, and recall by about 20% and 12% for MAHNOB-HCI and DEAP databases, respectively. Also, combining scalograms from four regions of pre-frontal, frontal, parietal, and parietal-occipital and two regions of frontal and parietal achieved the higher average accuracy of 77.47% and 87.45% for MAHNOB-HCI and DEAP databases, respectively. Conclusion: Combining CNN and MSVM increased the recognition of emotion from EEG signals and the results were comparable to state-of-the art studies.

Compared with the widely studied negative emotions in which different classes are easy to distinguish, nowadays less attention is paid to the recognition of positive emotions that are not fully independent. In this paper, we propose to recognize multiple positive emotions by analyzing brain activities and explore the neural representation of different positive emotions. Thirty-seven participants volunteered to participate in our study, in which their brain activities were recorded when watching five selected film clips. First, 150 well-known power features extracted from Electroencephalography (EEG) signals and 105 multimedia content analysis features were collected as the pool of candidate features. Second, based on the collected features, we propose to use a linear model and a nonlinear model to predict the percentage of five positive emotions. Then, percentage values were converted to ranking numbers and Kendall rank correlation coefficients were calculated. Our results showed that (1) ensemble of regressor chains using LSTM as unit regressor obtained both the best regression results and the best Kendall rank correlation coefficient on EEG features merely, and (2) top features from alpha frequency bands of EEG signals could represent different positive emotions. These results demonstrate the effectiveness of selective EEG features on recognizing different positive emotions.

A common drawback of EEG-based emotion recognition is that volume conduction effects of the human head introduce interchannel dependence and result in highly correlated information among most EEG features. These highly correlated EEG features cannot provide extra useful information, and they actually reduce the performance of emotion recognition. However, the existing feature selection methods, commonly used to remove redundant EEG features for emotion recognition, ignore the correlation between the EEG features or utilize a greedy strategy to evaluate the interdependence, which leads to the algorithms retaining the correlated and redundant features with similar feature scores in the EEG feature subset. To solve this problem, we propose a novel EEG feature selection method for emotion recognition, termed global redundancy minimization in orthogonal regression (GRMOR). GRMOR can effectively evaluate the dependence among all EEG features from a global view and then select a discriminative and nonredundant EEG feature subset for emotion recognition. To verify the performance of GRMOR, we utilized three EEG emotional data sets (DEAP, SEED, and HDED) with different numbers of channels (32, 62, and 128). The experimental results demonstrate that GRMOR is a promising tool for redundant feature removal and informative feature selection from highly correlated EEG features.

Numerous candidate EEG biomarkers have been put forward for use in clinical research on autism spectrum disorder (ASD), but biomarker development has been hindered by limited attention to the psychometric properties of derived variables, inconsistent results across small studies, and variable methodology. The authors evaluated the basic psychometric properties of a battery of EEG assays for their potential suitability as biomarkers in clinical trials.This was a large, multisite, naturalistic study in 6- to 11-year-old children who either had an ASD diagnosis (N=280) or were typically developing (N=119). The authors evaluated an EEG battery composed of well-studied assays of resting-state activity, face perception (faces task), biological motion perception, and visual evoked potentials (VEPs). Biomarker psychometrics were evaluated in terms of acquisition rates, construct performance, and 6-week stability. Preliminary evaluation of use was explored through group discrimination and phenotypic correlations.Three assays (resting state, faces task, and VEP) show promise in terms of acquisition rates and construct performance. Six-week stability values in the ASD group were moderate (intraclass correlations ≥0.66) for the faces task latency of the P1 and N170, the VEP amplitude of N1 and P1, and resting alpha power. Group discrimination and phenotype correlations were primarily observed for the faces task P1 and N170.In the context of a large-scale, rigorous evaluation of candidate EEG biomarkers for use in ASD clinical trials, neural response to faces emerged as a promising biomarker for continued evaluation. Resting-state activity and VEP yielded mixed results. The study's biological motion perception assay failed to display construct performance. The results provide information about EEG biomarker performance that is relevant for the next stage of biomarker development efforts focused on context of use.

Although evidence for cultural variants in facial expression decoding is accumulating, the other-race effect in facial expression processing and its neural correlates are still unclear. We investigated this question with a fully balanced design, in which a group of East Asian and a group of European Caucasian women categorized pictures of sad, happy, angry, and neutral facial expressions posed by individuals of their own-race and the other-race. Results revealed a disadvantage in categorizing expressions of anger in other-race faces in both samples, and for sad expressions in the European sample only. Partially consistent, East Asian participants showed longer latency of the N170 component in the event-related potential (ERP) and European Caucasian participants showed larger N170 amplitudes to other-race faces. The late positive complex in the ERP was less distinguishable among other-race facial expressions. Therefore, the present study observed an other-race effect in early and late stages of face processing, reflecting less efficient structural encoding and less elaborate processing for other-race than own-race faces.

Emotion recognition based on electroencephalography (EEG) is a significant task in the brain-computer interface field. Recently, many deep learning-based emotion recognition methods are demonstrated to outperform traditional methods. However, it remains challenging to extract discriminative features for EEG emotion recognition, and most methods ignore useful information in channel and time. This paper proposes an attention-based convolutional recurrent neural network (ACRNN) to extract more discriminative features from EEG signals and improve the accuracy of emotion recognition. First, the proposed ACRNN adopts a channel-wise attention mechanism to adaptively assign the weights of different channels, and a CNN is employed to extract the spatial information of encoded EEG signals. Then, to explore the temporal information of EEG signals, extended self-attention is integrated into an RNN to recode the importance based on intrinsic similarity in EEG signals. We conducted extensive experiments on the DEAP and DREAMER databases. The experimental results demonstrate that the proposed ACRNN outperforms state-of-the-art methods.

Now-a-days, due to mental stress a major section of society is affected by depression. There may be several reasons for depression especially in adults. As different person has different symptoms and its identification is a major challenge. Most of the people feel shy to accept that they are suffering from depression while some people are unaware of their depressed mental health. The objective of this paper is to design and develop an effective tool or model to diagnose depression. In this work, a hybrid system is designed and simulated for detecting depression using EEG features as well as facial features as biological feature gives accurate diagnosis. In this paper a deep learning approach termed as bidirectional long short-term memory (BiLSTM) is proposed with feature fusion from EEG data as well as facial data. The result analysis shows comparative analysis with other existing model and shows effectiveness of the proposed model.

Theories on controlled semantic cognition assume that word concreteness and linguistic context interact during semantic word processing. Methodological approaches and findings on how this interaction manifests at the electrophysiological and behavioral levels are heterogeneous. We measured ERPs and RTs applying a validated cueing paradigm with 19 healthy participants, who performed similarity judgments on concrete or abstract words (e.g., “butterfly” or “tolerance”) after reading contextual and irrelevant sentential cues. Data-driven analyses showed that concreteness increased and context decreased negative-going deflections in broadly distributed bilateral clusters covering the N400 and N700/late positive component time range, whereas both reduced RTs. Crucially, within a frontotemporal cluster in the N400 time range, contextual (vs. irrelevant) information reduced negative-going amplitudes in response to concrete but not abstract words, whereas a contextual cue reduced RTs only in response to abstract but not concrete words. The N400 amplitudes did not explain additional variance in the RT data, which showed a stronger contextual facilitation for abstract than concrete words. Our results support separate but interacting effects of concreteness and context on automatic and controlled stages of contextual semantic processing and suggest that effects on the electrophysiological versus behavioral level obtained with this paradigm are dissociated.

Sleep disturbances are prominent in drug use disorders, including those involving opioids in both humans and animals. Recent studies have shown that administration of liraglutide, a glucagon-like peptide-1 agonist, significantly reduces heroin taking and seeking in rats. In an effort to further understand the action of this substance on physiological functions and to evaluate safety issues for its potential clinical use, the aim of the present study was to determine whether the dose of liraglutide found effective in reducing responding for an opioid also could improve sleep in drug-naïve rats.Using a within-subjects design, adult male rats chronically implanted with EEG and EMG electrodes received subcutaneous injection of saline or 0.06, 0.10, 0.30 or 0.60 mg/kg liraglutide. The 0.10 and 0.30 mg/kg doses are known to be most effective in reducing responding for heroin in rats at light or dark onset during a 12:12 h light-dark cycle (0.10 mg/kg for taking and seeking, 0.30 mg/kg for seeking). EEG and EMG were recorded across the 24 h period following each injection.After both dark and light onset injections, liraglutide dose-dependently decreased wakefulness and increased non-rapid eye movement (NREM) sleep except at the lowest dose. The bout length of wakefulness and NREM sleep were decreased and increased, respectively. Whether administered at light or dark onset, the above alterations occurred primarily during the dark period (i.e., during the active period). The animals' body weight was decreased after liraglutide treatments as expected since it is clinically used for the treatment of obesity.These data indicate that liraglutide, at doses known to reduce responding for heroin and fentanyl, also increases NREM sleep, suggesting that the increase in sleep may contribute to the protective effects of liraglutide and may promote overall general health.

The combination of d-methylphenidate and guanfacine (an α-2A adrenergic agonist) may be an effective alternative to either agent as monotherapy in children with attention-deficit/hyperactivity disorder (ADHD). This study investigated the neural mechanisms underlying medication effects using cortical source analysis of electroencephalography (EEG) data.A total of 172 children with ADHD (aged 7-14; 118 boys) completed an 8-week randomized, double-blind, comparative study with 3 treatment arms: d-methylphenidate, guanfacine, or their combination. EEG modulations of brain oscillations at baseline and end point were measured during a spatial working memory task from cortical sources localized within the anterior cingulate (midfrontal) and primary visual cortex (midoccipital), based on previously reported ADHD and control differences. Linear mixed models examined treatment effects on EEG and performance measures.Combined treatment decreased midoccipital EEG power across most frequency bands and task phases. Several midoccipital EEG measures also showed significantly greater changes with combined treatment than with monotherapies. D-methylphenidate significantly increased midoccipital theta during retrieval, while guanfacine produced only trend-level reductions in midoccipital alpha during maintenance and retrieval. Task accuracy improved with combined treatment, was unchanged with d-methylphenidate, and worsened with guanfacine. Treatment-related changes in midoccipital power correlated with improvement in ADHD severity.These findings show that combined treatment ameliorates midoccipital neural activity associated with treatment-related behavioral improvements and previously implicated in visuo-attentional deficits in ADHD. Both monotherapies had limited effects on EEG measures, with guanfacine further showing detrimental effects on performance. The identified midoccipital EEG profile may aid future treatment monitoring for children with ADHD.Single Versus Combination Medication Treatment for Children With Attention Deficit Hyperactivity Disorder (Project1); https://clinicaltrials.gov/; NCT00429273.We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure sex and gender balance in the recruitment of human participants. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. We actively worked to promote sex and gender balance in our author group.

Mental imagery (MI) is gaining attention as a strategy towards endogenous brain stimulation for improving motor skill. Neurofeedback (NF) is commonly used to guide MI in order to activate the relevant brain networks. The current study investigates an individualized EEG-based method for NF through broad consideration of interactions between different brain networks. We selected the change in brain functional connectivity (FC) as an objective neurophysiological measure of change in motor skill during a longitudinal physical training (PT) program. Digital tracing tasks were developed for skill training and the spatial error in tracing was used to gauge the change in skill. We used partial least squares algorithms to find the most robust contributing networks towards correlation between the resting state FC and the acquired motor skill. We used the network with the largest margin for increasing FC as the candidate for NF training while experimenting with MI during a neurofeedback training program. The participant was informed of the changes in instantaneous FC through real-time audio feedback to help guide the MI. We showed over 20% reduction in tracing error through neurofeedback training alone, without any additional PT. We also showed retention of improvement in skill for several days after the completion of neurofeedback training. Our proposed methodology shows promise for a highly individualized approach towards improvement in motor skill. Given that EEG is an accessible health and wellness technology, such a method could provide a practical complementary option towards personalized therapeutic strategies to improve motor function., IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, ISSN:1534-4320, ISSN:1558-0210

The individual differences and the dynamic uncertain relationships among different electroencephalogram (EEG) regions are essential factors that limit EEG emotion recognition. To address these issues, in this paper, we propose a variational instance-adaptive graph method (V-IAG) that simultaneously captures the individual dependencies among different EEG electrodes and estimates the underlying uncertain information. Specifically, we employ two branches, i.e., instance-adaptive branch and variational branch, to construct the graph. Inspired by the attention mechanism, the instance-adaptive branch generates the graph based on the input so as to characterize the individual dependencies among EEG channels. The variational branch generates the probabilistic graph, which quantifies the uncertainties. We combine these two types of graphs to extract more discriminative features. To present more precise graph representation, we propose a new operation named the multi-level and multi-graph convolution operation, which aggregates the features of EEG channels from different frequencies with different graphs. Furthermore, we design the graph coarsening and employ the sparse constraint to obtain more robust features. We conduct extensive experiments on two widely-used EEG emotion recognition databases, i.e., SJTU emotion EEG dataset (SEED) and Multi-modal physiological emotion recognition dataset (MPED). The results demonstrate that the proposed model achieves the-state-of-the-art performance.

Emotional conflict control is impaired in major depression disorders (MDDs) and affects decision-making with further consequent social interactions dysfunction. However, neural correlates of conflict monitoring processes being modulated by different affective distractor stimuli are not clear in MDDs. In this paper, we investigated abnormal neural basis of conflict monitoring processes in MDD patients by applying dynamic causal modeling (DCM) technique on electroencephalography (EEG). The results indicated that MDD patients showed lower N2 amplitudes regardless of stimulus conditions, and reduced activation within ACC region for incongruent stimuli, relative to healthy controls. Especially, MDDs had more negative N2 amplitudes to happy incongruent trials than happy congruent trials. Source localization analyses revealed that MDD patients had significantly enhanced left inferior temporal gyrus (ITG) activation, which is involved in written words processing. Further DCM analysis provided abnormal neural correlates through greater backward connections (fusiform→ITG, amygdala→ITG) on happy incongruent trials than happy congruent trials in MDD group. These findings indicate that only sad words induce significantly greater interference effects to positive target faces in MDD patients, which may be associated with ITG activity dysfunction. The findings may share new insights into the neural mechanisms of emotional conflict processing in MDDs.

Absorption in mind-wandering (MW) may worsen our mood and can cause psychological disorders. Researchers indicate the possibility that meta-awareness of MW prevents these mal-effects and enhances favorable consequences of MW, such as boosting creativity; thus, meta-awareness has attracted psychological and clinical attention. However, few studies have investigated the nature of meta-awareness of MW, because there has been no method to isolate and operate this ability. Therefore, we propose a new approach to manipulate the ability of meta-awareness. We used Pavlovian conditioning, tying to it an occurrence of MW and a neutral tone sound inducing the meta-awareness of MW. To perform paired presentations of the unconditioned stimulus (neutral tone) and the conditioned stimulus (perception accompanying MW), we detected participants' natural occurrence of MW via electroencephalogram and a machine-learning estimation method. The double-blinded randomized controlled trial with 37 participants found that a single 20-min conditioning session significantly increased the meta-awareness of MW as assessed by behavioral and neuroscientific measures. The core protocol of the proposed method is real-time feedback on participants' neural information, and in that sense, we can refer to it as neurofeedback. However, there are some differences from typical neurofeedback protocols, and we discuss them in this paper. Our novel classical conditioning is expected to contribute to future research on the modulation effect of meta-awareness on MW.

With the increasing computing power, it is easier to develop machine learning and signal processing technologies. This led to massive significance in EEG-based Brain-Machine Interfaces that can be applied to a variety of sensing devices that can profit from the hands-free and individual conversion aspect of this field. Though, almost all of the investigation has been at the stage of proof-of-concept rather than at a more practical stage. As a result, there have been few innovations in techniques that enable people with seriously impaired nerve system (humans with Multiple Sclerosis, Amyotrophic Lateral Sclerosis, etc.) to navigate spatially in a simulated 2D or 3D environment. Newer technologies and growing trends in the computerization and artificial intelligence present a challenge to the ever-growing large community to introduce this new technology. This article will address the practicalities of the differences in signal processing and the significance of these innovations to the EEG- BMI in concurrent.

Background. Epilepsy is often diagnosed through clinical description, but inter-observer interpretations can be diverse and misleading.Objective. To assess the utility of smartphone videos in the diagnosis of paediatric epilepsy.Methods. The literature was reviewed for evidence to support the use of smartphone videos, inclusive of advantages, ethical practiceand potential disadvantages. An existing adult-based quality of video (QOV) scoring tool was adapted for use in children. A pilot studyused convenience sampling of videos from 25 patients, which were reviewed to assess the viability of the adapted QOV tool againstthe subsequent diagnosis for the patients with videos. The referral mechanism of the videos was reviewed for the source and consentprocesses followed.Results. A total of 14 studies were identified. Methodologies varied; only three focused on videos of children, and QOV was formally scored in three. Studies found that smartphone videos of good quality assisted the differentiation of epilepsy from non-epileptic events, especially with accompanying history and with more experienced clinicians. The ethics and risks of circulation of smartphone videos were briefly considered in a minority of the reports. The pilot study found that the adapted QOV tool correlated with videos of moderate and high quality and subsequent diagnostic closure.Conclusions. Data relating to the role of smartphone video of events in children is lacking, especially from low- and middle-incomesettings. Guidelines for caregivers to acquire good-quality videos are not part of routine practice. The ethical implications of transfer ofsensitive material have not been adequately addressed for this group. Prospective multicentre studies are needed to formally assess the viability of the adapted QOV tool for paediatric videos.

BACKGROUND: Previous studies have reported that epileptiform activity may be detectible in nearly half of patients with Alzheimer's disease (AD) on long-term electroencephalographic (EEG) recordings. However, such recordings can be uncomfortable, expensive, and difficult. Ear-EEG has shown promising results for long-term EEG monitoring, but it has not been used in patients with AD.OBJECTIVE: To investigate if ear-EEG is a feasible method for long-term EEG monitoring in patients with AD.METHODS: In this longitudinal, single-group feasibility study, ten patients with mild to moderate AD were recruited. A total of three ear-EEG recordings of up to 48 hours three months apart for six months were planned.RESULTS: All patients managed to wear the ear-EEG for at least 24 hours and at least one full night. A total of 19 ear-EEG recordings were performed (self-reported recording, mean: 37.15 hours (SD: 8.96 hours)). After automatic pre-processing, a mean of 27.37 hours (SD: 7.19 hours) of data with acceptable quality in at least one electrode in each ear was found. Seven out of ten participants experienced mild adverse events. Six of the patients did not complete the study with three patients not wanting to wear the ear-EEG anymore due to adverse events.CONCLUSION: It is feasible and safe to use ear-EEG for long-term EEG monitoring in patients with AD. Minor adjustments to the equipment may improve the comfort for the participants.

This study aimed to determine the correlation between outcomes following adrenocorticotrophic hormone (ACTH) therapy and measurements of relative power spectrum (rPS), weighted phase lag index (wPLI), and graph theoretical analysis on pretreatment electroencephalography (EEG) in infants with non-lesional infantile epileptic spasms syndrome (IESS).Twenty-eight patients with non-lesional IESS were enrolled. Outcomes were classified based on seizure recurrence following ACTH therapy: seizure-free (F, n = 21) and seizure-recurrence (R, n = 7) groups. The rPS, wPLI, clustering coefficient, and betweenness centrality were calculated on pretreatment EEG and were statistically analyzed to determine the correlation with outcomes following ACTH therapy.The rPS value was significantly higher in the delta frequency band in group R than in group F (p 0.001). The wPLI values were significantly higher in the delta, theta, and alpha frequency bands in group R than in group F (p = 0.007,0.001, and0.001, respectively). The clustering coefficient in the delta frequency band was significantly lower in group R than in group F (p 0.001).Our findings demonstrate the significant differences in power and functional connectivity between outcome groups.This study may contribute to an early prediction of ACTH therapy outcomes and thus help in the development of appropriate treatment strategies.

OBJECTIVE Resection and disconnection surgeries for epilepsy in the pediatric demographic (patients ≤ 18 years of age) are two separate, definitive intervention options in medically refractory cases. Questions remain regarding the role of surgery when seizures persist after an initial incomplete surgery. The aim of this study was to review the contemporary literature and summarize the metadata on the outcomes of repeat surgery in this specific demographic. METHODS Searches of seven electronic databases from inception to July 2022 were conducted using PRISMA guidelines. Articles were screened using prespecified criteria. Metadata from the articles were abstracted and pooled by random-effects meta-analysis of proportions. RESULTS Eleven studies describing 12 cohorts satisfied all criteria, reporting outcomes of 170 pediatric patients with epilepsy who underwent repeat resection or disconnection surgery. Of these patients, 55% were male, and across all studies, median ages at initial and repeat surgeries were 7.2 and 9.4 years, respectively. The median follow-up duration after repeat surgery was 47.7 months. The most commonly reported etiology for epilepsy was cortical dysplasia. Overall, the estimated incidence of complete seizure freedom (Engel class I) following repeat surgery was 48% (95% CI 40%–56%, p value for heterogeneity = 0.93), and the estimated incidence of postoperative complications following repeat surgery was 25% (95% CI 12%–39%, p = 0.04). There were six cohorts each that described outcomes for repeat resection and repeat disconnection surgeries. There was no statistical difference between these two subgroups with respect to estimated incidence of complete seizure freedom (p value for interaction = 0.92), but postoperative complications were statistically more common following repeat resection (p ≤ 0.01). CONCLUSIONS For both resection and disconnection surgeries, repeat epilepsy surgery in children is likely to confer complete seizure freedom in approximately half of the patients who experience unsuccessful initial incomplete epilepsy surgery. More data are needed to elucidate the impact on efficacy based on surgical approach selection. Judicious discussion and planning between the patient, family, and a multidisciplinary team of epilepsy specialists is recommended to optimize expectations and outcomes in this setting.

Over the past decade, emotion detection using rhythmic brain activity has become a critical area of research. The asymmetrical brain activity has garnered the most significant level of research attention due to its implications for the study of emotions, including hemispheric asymmetry or, more generally, asymmetrical brain activity. This study aimed at enhancing the accuracy of emotion detection using Electroencephalography (EEG) brain signals. This happens by identifying electrodes where relevant brain activity changes occur during the emotions and by defining pairs of relevant electrodes having asymmetric brain activities during emotions. Experimental results showed that the proposed method is highly competitive compared with existing studies of multi-class emotion recognition. These results were improved by processing not the whole EEG signals but by focusing on fragments of the signals, called epochs, which represent the instants where the excitation is maximum during emotions. The epochs were extracted using the zero-time windowing method and the numerator group-delay function.

Alzheimer's disease (AD), a neurodegenerative disorder characterized by progressive cognitive decline, is generally prevalent in elderly people with significant disability and mortality. There is no effective treatment for AD currently, but the early diagnosis might be beneficial for delaying the disease progression. Apart from invasive laboratory tests and expensive neuroimaging examination, the electroencephalography (EEG) and event related potentials (ERPs) have emerged as promising approaches for the early detection of AD as well as mild cognitive impairment (MCI), due to its affordability, noninvasively, and superior temporal resolution. In addition, the recent advent of deep learning architectures further improves the accuracy of AD and MCI diagnosis. This article reviewed the application of EEG signal for the early diagnosis of AD and MCI, especially focusing on ERPs and deep learning. Furthermore, recommendation for further research to recruit the combination of ERP components and deep leaning models in diagnosing AD and MCI was proposed and highlighted.

As a kind of biological network, the brain network conduces to understanding the mystery of high-efficiency information processing in the brain, which will provide instructions to develop efficient brain-like neural networks. Large-scale dynamical functional network connectivity (dFNC) provides a more context-sensitive, dynamical, and straightforward sight at a higher network level. Nevertheless, dFNC analysis needs good enough resolution in both temporal and spatial domains, and the construction of dFNC needs to capture the time-varying correlations between two multivariate time series with unmatched spatial dimensions. Effective methods still lack. With well-developed source imaging techniques, electroencephalogram (EEG) has the potential to possess both high temporal and spatial resolutions. Therefore, we proposed to construct the EEG large-scale cortical dFNC based on brain atlas to probe the subtle dynamic activities in the brain and developed a novel method, that is, wavelet coherence-S estimator (WTCS), to assess the dynamic couplings among functional subnetworks with different spatial dimensions. The simulation study demonstrated its robustness and availability of applying to dFNC. The application in real EEG data revealed the appealing ``Primary peak'' and ``P3-like peak'' in dFNC network properties and meaningful evolutions in dFNC network topology for P300. Our study brings new insights for probing brain activities at a more dynamical and higher hierarchical level and pushing forward the development of brain-inspired artificial neural networks. The proposed WTCS not only benefits the dFNC studies but also gives a new solution to capture the time-varying couplings between the multivariate time series that is often encountered in signal processing disciplines.

Background In the past twenty years, there has been an increasing interest among neuroscientists and physicians in mapping the cortical areas involved in the epileptogenic zone (EZ) through event-related potentials (ERPs) that enable the evaluation of the functional preservation of these areas. The present review is an update on publications on this topic. Objective To investigate the accuracy of the cognitive evoked of the medial temporal lobe P300 (MTL-P300) potential in detecting the EZ in temporal lobe epilepsy (TLE). Methods The systematic review of articles on the PubMed, Embase and Lilacs databases was conducted between February and December 2020. Articles published in English from 1985 to December 2020 were included. Additional studies were identified by searching the references of the selected studies and review articles. The studies were included for the following reasons: in-depth intracranial electroencephalography (iEEG) analysis of hippocampal activity; investigations of patients with TLE; and correlations between regarding the ERP results obtained in the temporal regions (MTL-P300) and the EZ. Results In the three studies analyzed, the authors were able to define the laterality of the EZ during the preoperative investigation through the MTL-P300 results. The sensitivity of this method was of ~ 70% to 80%, and the specificity between 70% and 94.7%. One of the limitations of the present review was the low number of studies. Conclusion There is evidence that the reduced amplitude of the MTL-P300 has high specificity in identifying the EZ, and this is a good marker for diagnosis in unilateral TLE. The low sensitivity and negative likelihood ratios negative that a normal MTL-P300 response does not exclude the epileptogenicity of the hippocampus. Resumo Antecedentes Nos últimos 20 anos, tem havido um crescente interesse de neurocientistas e médicos em mapear áreas corticais envolvidas na zona epileptogênica (ZE) por meio de potenciais relacionados a eventos (PREs), que permitem avaliar a preservação funcional dessas áreas. Esta revisão é uma atualização das publicações sobre esse tema. Objetivo Investigar a acurácia do potencial evocado cognitivo do lobo temporal medial P300 (medial temporal lobe P300, MTL-P300, em inglês) na detecção da ZE em casos de epilepsias do lobo temporal (ELT). Métodos A revisão sistemática de artigos nas bases de dados PubMed, Embase e Lilacs foi realizada entre fevereiro e dezembro de 2020. Foram incluídos artigos publicados em inglês de 1985 a dezembro de 2020. Estudos adicionais foram identificados por meio de busca nas referências dos estudos selecionados e artigos de revisão. Os estudos foram incluídos pelas seguintes razões: análise detalhada por meio de eletroencefalografia intracraniana (iEEG) da atividade hipocampal; investigações de pacientes com ELT; e correlações entre os resultados de ERP obtidos nas regiões temporais (MTL-P300) e na ZE. Resultados Nos três estudos analisados, os autores foram capazes de definir a lateralidade da ZE durante a investigação pré-operatória por meio dos resultados do MTL-P300. A sensibilidade deste método foi de 70% a 80%, e a especificidade, entre 70% e 94.7%. Uma das limitações desta revisão foi o baixo número de estudos. Conclusão Há evidências de que a amplitude reduzida do MTL-P300 tem alta especificidade na identificação da ZE, e este é um bom marcador para o diagnóstico na ELT unilateral. A baixa sensibilidade e a razão de verossimilhança negativa indicam que a resposta MTL-P300 normal não exclui a epileptogenicidade do hipocampo.

To develop an efficient brain-computer interface (BCI) system, electroencephalography (EEG) measures neuronal activities in different brain regions through electrodes. Many EEG-based motor imagery (MI) studies do not make full use of brain network topology. In this paper, a deep learning framework based on a modified graph convolution neural network (M-GCN) is proposed, in which temporal-frequency processing is performed on the data through modified S-transform (MST) to improve the decoding performance of original EEG signals in different types of MI recognition. MST can be matched with the spatial position relationship of the electrodes. This method fusions multiple features in the temporal-frequency-spatial domain to further improve the recognition performance. By detecting the brain function characteristics of each specific rhythm, EEG generated by imaginary movement can be effectively analyzed to obtain the subjects' intention. Finally, the EEG signals of patients with spinal cord injury (SCI) are used to establish a correlation matrix containing EEG channel information, the M-GCN is employed to decode relation features. The proposed M-GCN framework has better performance than other existing methods. The accuracy of classifying and identifying MI tasks through the M-GCN method can reach 87.456%. After 10-fold cross-validation, the average accuracy rate is 87.442%, which verifies the reliability and stability of the proposed algorithm. Furthermore, the method provides effective rehabilitation training for patients with SCI to partially restore motor function.

Previous experience often guides people's decision-making. How the corresponding consecutive outcomes in the gain-loss frame modulate feedback evaluation in dynamic situations remains unclear. In this study, we used event-related potentials (ERPs) with a Balloon Analogue Risk Task (BART) to investigate the effect of the consistency with previous feedback on current choices under the gain-loss frame and the neural mechanism by coding ERP signals evoked by consecutive outcomes among adults (N = 42). Results indicated that the framing effect on feedback-related negativity (FRN) amplitudes following current feedback in decision-making was insensitive to prior feedback. In contrast, the P300 amplitudes following two inconsistent consecutive rounds of feedback were greater than that following two consistent consecutive rounds of feedback in the gain frame, while this effect was absent in the loss frame, revealing a more significant valance-framing effect. These findings demonstrate that two inconsistent rounds of feedback enhance the framing effect, and suggest that the framing effect is sensitive to prior feedback in a late cognitive appraisal stage in decision-making under uncertainty, rather than in an early feedback processing stage. The present study provides novel insights into how consecutive outcomes in the gain-loss frame modulate feedback evaluation in dynamic situations.

This prospective observational study evaluated the long-term EEG changes in children treated with everolimus (EVO) for refractory TSC-associated epilepsy. Changes in EEG-abnormalities were related to developmental outcomes.Thirteen children treated with EVO were examined for EEG-recorded seizures and interictal epileptic discharges (IED) during a 72-hour-video-EEG-monitoring, which was performed at baseline and repeated at follow-up intervals of at least 9 months. Antiseizure medication was left unchanged for at least 27 months. Changes in cognitive developmental parameters were related to reduction of seizures and IED at the last monitoring.We found a significant reduction of recorded seizures and IED during sleep at the first as well as the last follow-up recording. The reduction of IED was especially prominent during sleep. For patients who continued for more than one monitoring under EVO (n = 8), number of seizures further decreased. In patients with developmental examination (n = 9), we observed that only (nearly) full cessation of IED was related to acquisition of new skills.In children with TSC, EVO was effective in reducing recorded seizures and IED; long-term EVO treatment led to a more pronounced reduction and an improvement of nocturnal IED even when the patient was initially not seizure-free. Cessation of IED in children with developmental improvement may point to the importance of healthy sleep for cognition.