Search

Background: Rumination, a tendency to focus on negative self-related thoughts, is a central symptom of depression. Studying the self-related aspect of such symptoms is challenging because of the need to distinguish self effects from the emotional content of task stimuli. This study employed an emotionally neutral self-related paradigm to investigate possible altered self-processing in depression and its link to rumination. Methods: People with major depressive disorder (n = 25) and controls (n = 25) underwent task-based electro-encephalogram recording. We studied late event-related potentials, along with low-frequency oscillatory power. We compared electroencephalogram metrics between groups and correlated them with depressive symptoms and reported rumination. Results: Participants with major depressive disorder displayed a difference in late positive potentials across frontocentral electrodes between self-related and non-self-related conditions. We found no such difference in controls. The magnitude of this difference was positively correlated with depressive symptoms and reported rumination. Participants with major depressive disorder also had elevated theta oscillation power at central electrodes in self-related conditions, a finding that we did not see in controls. Limitations: Patients with major depressive disorder were medicated at the time of the study. The group studied was primarily female, so the observed effects may have been sex-specific. Conclusion: Rumination appears to be linked to altered self-related processing in depression, independent of stimuli-related emotional confounds. This connection between self-related processing and depression may point to a self disorder as a core component of depression.

Background. Interest in electroencephalographic (EEG) coronavirus disease 2019 (COVID-19) findings has been growing, especially in the search for a specific-features EEG of encephalopathy. Methods. We made a retrospective analysis of 29 EEGs recorded in 15 patients with COVID-19 and neurological symptoms. We classified the EEGs as “Acute EEG” and “follow-up EEG.” We did a statistical analysis between voltage and respiratory status of the patient, stay or not in the intensive care unit (ICU), days of stay in the ICU, sedative drugs, pharmacological treatment, type of symptoms predominating, and outcome. Results. We found EEG abnormalities in all patients studied. We observed the amplitude of background

ObjectiveTo investigate the diagnostic utility of high frequency oscillations (HFOs) via scalp electroencephalogram (EEG) in infantile spasms.MethodsWe retrospectively analyzed interictal slow-wave sleep EEGs sampled at 2,000Hz recorded from 30 consecutive patients who were suspected of having infantile spasms. We measured the rate of HFOs (80-500Hz) and the strength of the cross-frequency coupling between HFOs and slow-wave activity (SWA) at 3-4Hz and 0.5-1Hz as quantified with modulation indices (MIs).ResultsTwenty-three patients (77%) exhibited active spasms during the overnight EEG recording. Although the HFOs were detected in all children, increased HFO rate and MIs correlated with the presence of active spasms (p&nbsp

In recent years, there are great research interests in using the Electroencephalogram (EEG) signals in biometrics applications. The strength of EEG signals as a biometric comes from its major fraud prevention capability. However, EEG signals are so sensitive, and many factors affect its usage as a biometric; two of these factors are the number of channels, and the required time for acquiring the signal; these factors affect the convenience and practicality. This study proposes a novel approach for EEG-based biometrics that optimizes the channels of acquiring data to only one channel. And the time to only one second. The results are compared against five commonly used classifiers named: KNN, Random Forest (RF), Support Vector Machine (SVM), Decision Tables (DT), and Naïve Bayes (NB). We test the approach on the public Texas data repository. The results prove the constancy of the approach for the eight minutes. The best result of the eyes-closed scenario is Average True Positive Rate (TPR) 99.1% and 98.2% for the eyes-opened. And it reaches 100% for multiple subjects.

Introduction: Some neurologic conditions that can quickly and with low costs be recognized, classified and treated thanks to the availability of an EEG recording in an emergency setting. However, although considered a cheap, not invasive, highly accurate diagnostic investigation, still today, an EEG recording in emergency, in real time during the event paroxysmal ictal phase, is not yet been become a routine.Areas covered: This review will cover the role and utility of EEG recording in the emergency setting, both in emergency department and intensive care unit, in adult and pediatric age, in people admitted for status epilepticus (convulsive or non-convulsive), paroxysmal non-epileptic events, or other conditions/diseases presenting with mental status changes.Expert opinion: The prompt recognition of some specific EEG-patterns can permit an immediate and appropriate therapeutic choice with the resolution of dramatic clinical pictures, which, if not recognized, sometimes could result in severe prognostic events with high mortality or neuropsychiatric disability. It is important in the next future, to improve the availability of these EEG digital continuous monitoring, which should be widely used in emergency settings, developing moreover tools and techniques permitting also review, analysis and EEG-reporting by experts who can work away from the hospital.

To test the feasibility of implementing multisensory (auditory and visual) stimulation in combination with electrodes placed on non-hair positions to design more efficient and comfortable Brain-computer interfaces (BCI). Fifteen volunteers participated in the experiments. They were stimulated by visual, auditory and multisensory stimuli set at 37, 38, 39 and 40Hz and at different phases (0°, 90°, 180° and 270°). The electroencephalogram (EEG) was measured from Oz, T7, T8, Tp9 and Tp10 positions. To evaluate the amplitude of the visual and auditory evoked potentials, the signal-to-noise ratio (SNR) was used and the accuracy of detection was calculated using canonical correlation analysis. Additionally, the volunteers were asked about the discomfort of each kind of stimulus. The multisensory stimulation allows for attaining higher SNR on every electrode. Non-hair (Tp9 and Tp10) positions attained SNR and accuracy similar to the ones obtained from occipital positions on visual stimulation. No significant difference was found on the discomfort produced by each kind of stimulation. The results demonstrated that multisensory stimulation can help in obtaining high amplitude steady-state evoked responses with a similar discomfort level. Then, it is possible to design a more efficient and comfortable hybrid-BCI based on multisensory stimulation and electrodes on non-hair positions. The current article proposes a new paradigm for hybrid-BCI based on steady-state evoked potentials measured from the area behind-the-ears and elicited by multisensory stimulation, thus, allowing subjects to achieve similar performance to the one achieved by visual-occipital BCI, but measuring the EEG on a more comfortable electrode location.

정량적 뇌파는 연구와 임상적 분야에서 활발하게 이용되어 다양한 임상적 증상과 인지기능의 자극 및 과제에 따른 대뇌의 생물학적인 바이오 마커를 규명하는 등 대뇌의 변화를 객관적으로 증명하는데 지속적으로 사용되고 있다. 뇌파에서 정량적 분석과 정성적 분석은 분석하는 방법이 다르기 때문에 측정 방법과 환경이 비슷하지만 한편으론 다르다. 정성적 분석은 뇌파를 판독하는 사람이 잡파를 제외시키고 볼 수 있지만 정량적 분석은 수학적 모델링을 기반으로 데이터의 모든 것을 포함하여 분석을 실시하고 있기 때문에 잡파가 결과에 영향을 준다. 병원에서 임상생리학적 검사인 뇌파를 담당하는 임상병리사들이 뇌파를 이용한 연구는 다른 분야에 비해서 아주 드물다. 이러한 현상은 임상검사과학 분야 중에 임상생리학적 검사에서 두드러지게 나타난다. 왜냐하면 현재 대학에서 임상생리학을 연구하는 실험실이 많지 않기 때문이다. 본 저자의 목적은 정량적 분석을 하고자 하는 임상병리사, 대학원생, 연구자들이 쉽게 접근하여 앞으로 뇌파의 많은 연구가 이루어 질 수 있는 기초자료로 활용되기를 기대하고, 앞으로 많은 대학에서 임상생리학 실험실이 생겨 많은 연구들이 이루어져 좋은 논문들이 많이 나오기를 기대해 본다

We aimed to reveal the anti-convulsant effects sulfasalazine and its mechanism in pentylenetetrazole (PTZ)-induced seizures in rats. Forty-eight male Wistar albino rats (200-250 g) were randomly divided into two groups: 24 for electroencephalography (EEG) recording (group A) and 24 for behavioral studies (group B). About 70 mg/kg PTZ was used for behavioral studies after sulfasalazine administration and 35 mg/kg PTZ was used for EEG recording after sulfasalazine administration. Electrodes were implanted on the dura mater over the left frontal cortex and the reference electrode was implanted over the cerebellum for EEG recording. Racine’s convulsion scale, first myoclonic jerk onset time, spike percentages, brain malondialdehyde (MDA), superoxide dismutase (SOD), and prostaglandin F2α (PGF2α) levels were evaluated between the groups. First myoclonic jerk onset time was significantly shorter in the saline group than both 250 and 500 mg/kg sulfasalazine groups (P

Highlights • Evaluation of epilepsy source extent estimation from MEG measurements. • FAST-IRES gave robust location and extent estimation under different noise levels. • Epileptic sources were estimated from interictal discharges in drug-resistant epilepsy patients. • FAST-IRES outperformed LCMV in both simulation and patient data analysis., Non-invasive MEG/EEG source imaging provides valuable information about the epileptogenic brain areas which can be used to aid presurgical planning in focal epilepsy patients suffering from drug-resistant seizures. However, the source extent estimation for electrophysiological source imaging remains to be a challenge and is usually largely dependent on subjective choice. Our recently developed algorithm, fast spatiotemporal iteratively reweighted edge sparsity minimization (FAST-IRES) strategy, has been shown to objectively estimate extended sources from EEG recording, while it has not been applied to MEG recordings. In this work, through extensive numerical experiments and real data analysis in a group of focal drug-resistant epilepsy patients’ interictal spikes, we demonstrated the ability of FAST-IRES algorithm to image the location and extent of underlying epilepsy sources from MEG measurements. Our results indicate the merits of FAST-IRES in imaging the location and extent of epilepsy sources for pre-surgical evaluation from MEG measurements.

Identifying the localization, distribution, and polarity of waveforms are the prime goals of clinical scalp EEG analysis. Appropriate choices of bipolar and referential montages are keys to emphasizing the diagnostic features of interest, and demand some understanding of the spatiotemporal physical behavior of the underlying neuronal generators. Several examples drawn from canonical epilepsy syndromes are used to illustrate this general message.

The lack of consistent findings in the research field of psychopathy unveils the heterogeneity of this personality structure and, therefore, a dissociation of its phenotypic components is a promising venue to conciliate paradoxical results. The current study aims to assess the dissociable effects of psychopathic traits on N2 and P3 modulation, while analyzing the stability of some endophenotypic processes across development. A sample of adolescents (n = 33) and adults (n = 31) completed, respectively, the Youth Psychopathy Inventory and the Triarchic Psychopathy Measure and performed the same Go/No-Go task during an EEG recording. For adolescents, the unemotionality and remorselessness subdimensions of the callous-unemotional (CU) factor predicted, respectively, reduced N2 and P3 amplitudes. This suggests impaired inhibitory control in both subdimensions of the broad CU factor. However, the callousness subdimension of the CU factor predicted increased N2 amplitude. This pattern of findings was also observed for meanness in adults, which leads to the hypothesis that callousness traits may be a specific precursor of meanness.

It has been suggested that individual differences in cortical excitability leading to disruption of the timing and integration of sensory information processing may explain why some people have out of body experiences (OBE) in the absence of any known pathological or psychiatric condition. Here we recorded EEG from people who either had, or had not experienced an OBE in order to investigate the neural dynamics of OBE in the non-clinical population. A screening questionnaire was completed by 551 people, 24% of whom reported having at least one OBE. Participants who were free of any psychiatric or neurological diagnoses, including migraines, were invited to take part in subsequent EEG recording. EEG data were obtained from 19 people who had had an OBE and 20 who had not. Amplitude of the visual P1 ERP deflection and consistency of alpha-band phase locking were significantly reduced in the participants who had had an OBE. We did not find any group differences in resting state power or in visually induced gamma oscillations. These results provide support for the claim that cortical differences, particularly with respect to the timing of visual information processing, may give rise to OBE in clinically healthy individuals. To our knowledge, this study is the first to compare EEG variables obtained from people who have, and have not, had an OBE.

The study presents a frequency domain, instead of a relative power intensity comparison framework domain, that evaluates the degree of anxiety and stress brain network activated in high beta oscillation (22-30 Hz), during a resting, EC basal condition of EEG recording. The parameter measured served as an evaluator of the brain spontaneous, self-generated, global de-synchronizing, workload endogenous attractors that displace the EEG oscillatory dynamics toward free energy-consuming processing loops. The distinction of the high beta EEG waves as related with stress and anxiety, together with an approach based primarily on the inter-channel frequency correlation, rather than only the power activation of the frequency beta range, allowed us to supplement with new finding and ways to evaluate the impact of self-generated processing attractors that kept our brain apart from natural tendency to re-synchronize, save and reorganize energy.

Aim: Migralepsy is a clinical entity that occasionally represents a diagnostic problem. An apparent history and clinical manifestation of migraine may mask the epileptic attack accompanying migralepsy. The aim of this study is to present our experience with clinical and electroencephalography (EEG) findings and treatment of our patients diagnosed with childhood migralepsy disease.Methods: We documented six patients who were initially followed-up with a diagnosis of migraine, subsequently observed to have epileptic seizures, and then diagnosed with migralepsy.Result: Our patients became asymptomatic by giving good responses to antiepileptic therapy based on clinical and electroencephalography (EEG) findings.Conclusions: This case series shows that EEG recording can be useful in all stages of migraine for long-term, safe monitoring. Identifying patients with possible migralepsy will enable them to receive antiepileptic treatment.

The self-reference effect in memory (SRE), in which stimuli related to self are better remembered than other stimuli, has been studied often in the fMRI literature, but much less with EEG. In two experiments, we investigated how self-referencing modulated event-related potential (ERP) markers of the subsequent memory effect, testing whether the same components that reflect memory success are impacted or whether unique components are modulated by self-referencing. Participants were asked to evaluate whether an adjective accurately described either the self or a given other by making a yes/no key press during EEG recording. Then participants were given a surprise recognition memory test where they judged each adjective as old or new. We observed a main effect of self-relevance on a late positivity at right frontal electrodes. A very similar effect was observed when comparing words subsequently remembered to those that were forgotten. However, no interaction was found between self-relevance and subsequent memory, suggesting the frontal positivity is not exclusive to the SRE, but instead a reflection of deeper encoding that leads to better memory. Thus, this frontal positivity may be a marker of a deeper encoding process that is elicited by self-referencing but not exclusive to the SRE.

Electroencephalography (EEG) is essential for the diagnosis of epilepsy, but it requires expertise and experience to identify abnormalities. It is thus crucial to develop automated models for the detection of abnormalities in EEGs related to epilepsy. This paper describes the development of a novel class of compact convolutional neural networks (CNNs) for detecting abnormal patterns and electrodes in EEGs for epilepsy. The designed model is inspired by a CNN developed for brain–computer interfacing called multichannel EEGNet (mEEGNet). Unlike the EEGNet, the proposed model, mEEGNet, has the same number of electrode inputs and outputs to detect abnormal patterns. The mEEGNet was evaluated with a clinical dataset consisting of 29 cases of juvenile and childhood absence epilepsy labeled by a clinical expert. The labels were given to paroxysmal discharges visually observed in both ictal (seizure) and interictal (nonseizure) durations. Results showed that the mEEGNet detected abnormalities with the area under the curve, F1-values, and sensitivity equivalent to or higher than those of existing CNNs. Moreover, the number of parameters is much smaller than other CNN models. To our knowledge, the dataset of absence epilepsy validated with machine learning through this research is the largest in the literature.

Simple Summary In this study, we investigated various types of application of head-only electrical stunning to turkeys, in order to improve the effectiveness of the stunning process. We used recordings of physical responses and of electroencephalograms (EEGs) to assess effectivity of stunning and various waveforms: alternating current (AC) and pulsed direct currents (DC). Depending on the waveforms, voltages between 125 and 250 V produced an effective stun in turkeys in this study. Abstract Electrical stunning is likely to remain an important stunning method for turkeys at slaughter. The purpose of this study is to understand the application of various waveforms of alternating current (AC) and pulsed direct currents (DC), head-only, to turkeys and to improve the effectiveness of handheld stunning of turkeys. We evaluated the effectiveness of stunning by documenting physical responses and recording electroencephalograms (EEGs). For the assessment of physical responses, the stunning voltage was varied depending on the proportion of animals effectively stunned at a certain voltage level. If all turkeys in a group of 10 were stunned, the voltage was decreased, and the next group was stunned. This was repeated until not all turkeys showed signs of being effectively stunned. The experiment was then repeated at the voltage level just above the one that showed incomplete effective stunning. The effects of the stunning on the EEG recording was assessed in 16 turkeys to measure the occurrence of epileptiform EEGs, in 14 turkeys to assess epileptiform EEGs after neck-cut (bleeding), and in 14 turkeys to assess the effect of increased voltage and reduced frequency on epileptiform EEGs. Assessing EEGs in a laboratory setting contributes considerably to the understanding of electrical stunning procedures. Voltages between 125 and 250 V, depending on the waveform assessed, were effective in producing an effective stun in turkeys in this study.

Electroencephalography (EEG) of neonatal patients is amongst the most valuable diagnostic and prognostic tool. EEG recordings, acquired at the bedside of infants, evaluate brain function and the maturation of premature and extremely premature infants. Strict conditions of acquisition and interpretation must be respected to guarantee the quality of the EEG and ensure its safety for fragile children. This article provides guidance for EEG acquisition including: (1) the required equipment and devices, (2) the modalities of installation and asepsis precautions, and (3) the digital signal acquisition parameters to use during the recording. The fundamental role of a well-trained technician in supervising the EEG recording is emphasized. In parallel to the acquisition recommendations, we present a guideline for EEG interpretation and reporting. The successive steps of EEG interpretation, from reading the EEG to writing the report, are described. The complexity of the EEG signal in neonates makes artefact detection difficult. Thus, we provide an overview of certain characteristic artefacts and detail the methods for eliminating them.

There is ongoing discussion whether the cellular or mobile phone usage causes any health effects. The aim of this research is to investigate the effects of mobile phone usage on human brainwaves using electroencephalograph (EEG). The brainwave signals were analyzed using Statistical Package for the Social Sciences (SPSS). Thirty samples were interviewed prior to EEG recording. Then, the EEG recordings were performed for three sessions; before, during and after the phone calls. The findings show that alpha is dominant compared to the other frequency bands. In addition, value for the left brainwaves always higher than the right for all frequency bands which means that the samples were left brain dominant. The overall correlation between left and right brainwaves signal for all bands shows decrement for during and after phone calls, thus reducing brainwaves balancing. Therefore, there is evidence that the usage of mobile phones affect the brainwaves.

Motor imagery (MI) is one of the important brain-computer interface (BCI) paradigms, which can be used to control peripherals without external stimulus. Imagining the movements of different joints of the same limb allows intuitive control of the outer devices. In this report, we describe an open access multi-subject dataset for MI of different joints from the same limb. This experiment collected data from twenty-five healthy subjects on three tasks: 1) imagining the movement of right hand, 2) imagining the movement of right elbow, and 3) keeping resting with eyes open, which results in a total of 22,500 trials. The dataset provided includes data of three stages: 1) raw recorded data, 2) pre-processed data after operations such as artifact removal, and 3) trial data that can be directly used for feature extraction and classification. Different researchers can reuse the dataset according to their needs. We expect that this dataset will facilitate the analysis of brain activation patterns of the same limb and the study of decoding techniques for MI., Measurement(s)brain measurement • functional brain measurementTechnology Type(s)electroencephalography (EEG) • silver/silver chloride reference electrodeFactor Type(s)type of motion imagery taskSample Characteristic - OrganismHomo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12278444

Consciousness is central to animal welfare concerns. Its assessment is most often conducted based on behavior, with a poor understanding of the correspondence between behavior and the neurobiological processes that underlie the subjective experience of consciousness. Recording of brain electrical activity using electrodes placed under the skull improves EEG recording by minimizing artifacts from muscular or cardiac activities, and it can now be combined with wireless recording in free-moving animals. This experiment investigated the correspondence between wireless 'under the skull' epidural EEG and the behavior of 18 five-week-old female piglets undergoing nitrous oxide (N2O) or carbon dioxide (CO2) gradual fill gas euthanasia at 25% replacement rate per minute of the chamber volume. Piglets exposed to CO2 had a peak in EEG total power ('Ptot') during the flailing stage, whereas piglets exposed to N2O had a higher EEG 95% spectral edge frequency ('F95') during their initial explorative behavior phase and a drop in EEG median frequency ('F50') after loss of posture. Loss of posture without righting attempt, as the last behavioral state observed during euthanasia, preceded the onset of transitional EEG on average by 0.9 and 3.1 min (for CO2 and N2O treatments, respectively), and the onset of isoelectric EEG by 4.5 and 6.2 min (for CO2 and N2O treatments, respectively). Paddling movements occurred shortly before and during transitional EEG but never during isoelectric EEG, whereas gasps persisted after the EEG had become isoelectric. The dynamics of EEG spectral changes were complex to interpret in relation to the degree of consciousness, but isoelectric EEG as an unequivocal indicator of unconsciousness appeared several minutes after loss of posture with no righting attempt. This leaves a window of uncertainty in regards to the potential for consciousness after loss of posture during gradual fill gas euthanasia in piglets.

ObjectiveTo investigate the diagnostic utility of high frequency oscillations (HFOs) via scalp electroencephalogram (EEG) in infantile spasms.MethodsWe retrospectively analyzed interictal slow-wave sleep EEGs sampled at 2,000 Hz recorded from 30 consecutive patients who were suspected of having infantile spasms. We measured the rate of HFOs (80-500 Hz) and the strength of the cross-frequency coupling between HFOs and slow-wave activity (SWA) at 3-4 Hz and 0.5-1 Hz as quantified with modulation indices (MIs).ResultsTwenty-three patients (77%) exhibited active spasms during the overnight EEG recording. Although the HFOs were detected in all children, increased HFO rate and MIs correlated with the presence of active spasms (p < 0.001 by HFO rate; p < 0.01 by MIs at 3-4 Hz; p = 0.02 by MIs at 0.5-1 Hz). The presence of active spasms was predicted by the logistic regression models incorporating HFO-related metrics (AUC: 0.80-0.98) better than that incorporating hypsarrhythmia (AUC: 0.61). The predictive performance of the best model remained favorable (87.5% accuracy) after a cross-validation procedure.ConclusionsIncreased rate of HFOs and coupling between HFOs and SWA are associated with active epileptic spasms.SignificanceScalp-recorded HFOs may serve as an objective EEG biomarker for active epileptic spasms.HighlightsObjective analyses of scalp high frequency oscillations and its coupling with slow-wave activity in infantile spasms were feasible.Increased rate of high frequency oscillations and its coupling with slow-wave activity correlated with active epileptic spasms.The scalp high frequency oscillations were also detected in neurologically normal children (although at the low rate).

Background Human clinical studies reported that several electroencephalographical (EEG) parameters can be used as biomarkers of psychiatric disorders. EEGs recorded from non-human primates (monkeys) is useful for understanding of human pathologies of psychiatric disorders and development of new therapeutic agents. New methods In this study, we expand a previous non-invasive head holding system with face masks for awake monkeys to be applied to scalp EEG recording. The new design of a head holding system allows to attach scalp EEG electrodes on the positions comparable to human electrode placement and to present auditory stimuli. Results With this system, we could record auditory evoked potentials (AEPs) in auditory sensory gating and oddball paradigms, which are often used as biomarkers of psychiatric disorders in animal models and human patients. The recorded AEPs were comparable to previous human clinical data. Comparison with existing methods Compared with previous non-invasive head holding systems, top, side (cheek and ears), and rear of the head can be open for attachment of EEG electrodes and auditory stimulation in the present system. Conclusions The results suggest that the present system is useful in EEG recording from awake monkeys. Furthermore, this system can be applied to eye-tracking and chronic intra-cerebral recording experiments., Neuroscience; Neurology; Physiology; Behavioral test; Cognition; Consciousness; Mental disorder; Veterinary medicine; Biological psychiatry; Non-invasive head holding; Pre-clinical translational research; EEG; Non-human primates

Background Electroencephalography (EEG) measured during transcranial direct current stimulation (tDCS) can help understand the accurate impact of tDCS on the brain, but this has been hindered due to significant inflow of tDCS-induced electrical artifacts. Objective In this study, we introduce a novel tDCS device developed based on current limiter, which can prevent the generation of significant electrical artifacts. Methods To verify the feasibility of our developed tDCS device, we performed simultaneous measurement of EEG during tDCS application with five different current intensities (0, 500, 1,000, 1,500, and 2,000 μA). Changes in EEG power spectral density (PSD) and correlation between the PSD of non-stimulation and tDCS condition were investigated to see whether our tDCS device can be used for simultaneous EEG recording without significant inflow of tDCS-induced electrical artifacts. Resutls The mean EEG-PSD differences between non-stimulation and tDCS condition were not significant for all stimulation current intensities. Furthermore, EEG-PSDs estimated during non-stimulation and tDCS application showed statistically high correlation for all comparison cases. Conclusion Based on the results, we could demonstrate the feasibility of our tDCS device based on current limiter for simultaneous EEG measurement, which could potentially provide a way to investigate the impact of tDCS on the brain more accurately.

Background Limited-channel EEG research in neonates is hindered by lack of open, accessible analytic tools. To overcome this limitation, we have created the Washington University-Neonatal EEG Analysis Toolbox (WU-NEAT), containing two of the most commonly used tools, provided in an open-source, clinically-validated package running within MATLAB. Methods The first algorithm is the amplitude-integrated EEG (aEEG), which is generated by filtering, rectifying and time-compressing the original EEG recording, with subsequent semi-logarithmic display. The second algorithm is the spectral edge frequency (SEF), calculated as the critical frequency below which a user-defined proportion of the EEG spectral power is located. The aEEG algorithm was validated by three experienced reviewers. Reviewers evaluated aEEG recordings of fourteen preterm/term infants, displayed twice in random order, once using a reference algorithm and again using the WU-NEAT aEEG algorithm. Using standard methodology, reviewers assigned a background pattern classification. Inter/intra-rater reliability was assessed. For the SEF, calculations were made using the same fourteen recordings, first with the reference and then with the WU-NEAT algorithm. Results were compared using Pearson's correlation coefficient. Results For the aEEG algorithm, intra- and inter-rater reliability was 100% and 98%, respectively. For the SEF, the mean±SD Pearson correlation coefficient between algorithms was 0.96±0.04. Conclusion We have demonstrated a clinically-validated toolbox for generating the aEEG as well as calculating the SEF from EEG data. Open-source access will enable widespread use of common analytic algorithms which are device-independent and unlikely to become outdated as technology changes, thereby facilitating future collaborative research in neonatal EEG.

Neuroimaging is crucial for stroke diagnosis and to establish the feasibility of reperfusion therapy, but is not practical for the assessment of continuous evolution of brain ischemia. Electroencephalography (EEG) in the early phase of brain ischemia could be a feasible instrument of functional monitoring. In this context, it would be of great research and clinical interest to assess the relation between EEG parameters and the hypoperfused volume measured by Computed Tomography Perfusion (CTP), as possible real-time surrogate parameters for extent of brain ischemia. This preliminary study aimed at investigating the relation between stroke-related EEG changes, measured on bedside with wireless EEG device, and the extent of hypoperfused volume assessed on CTP during the hyper-acute phase. We studied 12 consecutive ischemic stroke patients who underwent CTP assessment and EEG recording with wireless device within 4.5 hours from symptom onset. Total hypoperfused volume correlated significantly with the delta/alpha power ratio (ρ=0.72; p

espanolIntroduccion El estres durante la pubertad ejerce efectos a largo plazo sobre sistemas endocrinos y estructuras cerebrales como corteza prefrontal (CPF) y amigdala basolateral (ABL). Ambas estructuras participan en la modulacion de la conducta sexual y su funcionamiento es regulado por androgenos. Objetivo Evaluar los efectos del estres puberal por aislamiento social sobre la motivacion sexual, los niveles sericos de testosterona y la actividad electroencefalografica (EEG) de la CPF y ABL en ratas macho. Metodo Se utilizaron sesenta ratas macho sexualmente expertas, 30 fueron estresadas por aislamiento social durante la pubertad (GE, hospedados 1 por caja, dias 25 al 50 postnatal), y el resto conformo el grupo control (GC, hospedados 5 por caja). Las ratas fueron implantadas bilateralmente en la CPF y ABL y el EEG fue registrado durante estado vigilia-quieto en dos condiciones: sin motivacion sexual (SMS) y con motivacion sexual (MS). Finalmente, las ratas se sacrificaron por decapitacion para medir los niveles de testosterona. Resultados El GE presento menor motivacion sexual, menores niveles de testosterona y, en presencia de una hembra receptiva, presentaron una mayor activacion EEG amigdalina, mientras que el GC mostro una mayor activacion EEG prefrontal. Discusion y conclusion Es probable que la disminucion de los niveles de testosterona como resultado del estres puberal haya afectado la funcionalidad prefrontal y amigdalina y, por ende, la motivacion sexual. Estos datos pudieran explicar algunos de los cambios hormonales y cerebrales asociados con alteraciones sexuales producidas por estres. Esta propuesta debera explorarse en futuras investigaciones animales y clinicas. EnglishIntroduction: stress during puberty exerts long-term effects on endocrine systems and brain structures, such as the prefrontal cortex (PFC) and basolateral amygdala (BLA), two cerebral areas that participate in modulating sexual behavior and whose functioning is regulated by androgenic hormones. Objective: to evaluate the effect of pubertal stress due to social isolation on the sexual motivation, serum testosterone levels, and electroencephalographic activity (EEG) of the PFC and BLA in male rats. Method: sixty sexually-experienced male rats were used. Thirty were stressed by social isolation during puberty (SG, housed 1 per cage, postnatal days 25-50); the other 30 formed the control group (CG, 5 per cage). All rats were implanted bilaterally with stainless steel electrodes in the PFC and BLA. EEGs were recorded during the awake-quiet state in two conditions: without sexual motivation (WSM), and with sexual motivation (SM). After EEG recording, the rats were sacrificed by decapitation to measure their testosterone levels. Results: SG showed lower sexual motivation and testosterone levels, but higher amygdaline EEG activation in the presence of a receptive female, while CG showed higher prefrontal EEG activation. Discussion and conclusion: It is probable that the decreased testosterone levels resulting from pubertal stress affected prefrontal and amygdaline functionality and, hence, sexual motivation. These data could explain some of the hormonal and cerebral changes associated with stress-induced sexual alterations, though this suggestion requires additional clinical and animal research.

Electroencephalography (EEG) is one of the most widely used techniques to measure human brain activity. EEG recordings provide a direct, high temporal resolution measure of cortical activity from noninvasive scalp electrodes. However, the signals are small relative to the noise, and optimizing the quality of the recorded EEG data can significantly improve the ability to identify signatures of brain processing. This protocol provides a step-by-step guide to recording the EEG from human research participants using strategies optimized for producing the best quality EEG.

In this work we explored the use of linear and nonlinear, statistical approaches to studying basal EEG brain activity. To do this, we used the Moving Correlation Coefficient (MCC), and the Hurst exponent estimator to visualize short and long term EEG behavior during 2 minutes of EEG recording in closed eyes basal conditions. The EEG of 8 subjects served as data source for exploring the time series organization of the EEG in resting state. We found inter-subjects shared network patterns of high synchronic functional connectivity in frontal and right temporo-occipital regions. We also found a diversity of individual different networks of variable degree of synchronicity in the range of -0.5 0.5 for all channels. The short-term approximation to the chaos oscillation (M-Hurst) revealed several individual differences and a potential tool for subject’s characterization.

Brain–Computer Interfaces (BCIs) are real-time computer-based systems that translate brain signals into useful commands. To date most applications have been demonstrations of proof-of-principle; widespread use by people who could benefit from this technology requires further development. Improvements in current EEG recording technology are needed. Better sensors would be easier to apply, more confortable for the user, and produce higher quality and more stable signals. Although considerable effort has been devoted to evaluating classifiers using public datasets, more attention to real-time signal processing issues and to optimizing the mutually adaptive interaction between the brain and the BCI are essential for improving BCI performance. Further development of applications is also needed, particularly applications of BCI technology to rehabilitation. The design of rehabilitation applications hinges on the nature of BCI control and how it might be used to induce and guide beneficial plasticity in the brain.

Objective. Electroencephalography (EEG) is a key tool for non-invasive recording of brain activity and the diagnosis of epilepsy. EEG monitoring is also widely employed in rodent models to track epilepsy development and evaluate experimental therapies and interventions. Whereas automated seizure detection algorithms have been developed for clinical EEG, preclinical versions face challenges of inter-model differences and lack of EEG standardization, leaving researchers relying on time-consuming visual annotation of signals. Approach. In this study, a machine learning-based seizure detection approach, ‘Epi-AI’, which can semi-automate EEG analysis in multiple mouse models of epilepsy was developed. Twenty-six mice with a total EEG recording duration of 6451 h were used to develop and test the Epi-AI approach. EEG recordings were obtained from two mouse models of kainic acid-induced epilepsy (Models I and III), a genetic model of Dravet syndrome (Model II) and a pilocarpine mouse model of epilepsy (Model IV). The Epi-AI algorithm was compared against two threshold-based approaches for seizure detection, a local Teager-Kaiser energy operator (TKEO) approach and a global Teager-Kaiser energy operator-discrete wavelet transform (TKEO-DWT) combination approach. Main results. Epi-AI demonstrated a superior sensitivity, 91.4%–98.8%, and specificity, 93.1%–98.8%, in Models I–III, to both of the threshold-based approaches which performed well on individual mouse models but did not generalise well across models. The performance of the TKEO approach in Models I–III ranged from 66.9%–91.3% sensitivity and 60.8%–97.5% specificity to detect spontaneous seizures when compared with expert annotations. The sensitivity and specificity of the TKEO-DWT approach were marginally better than the TKEO approach in Models I–III at 73.2%–80.1% and 75.8%–98.1%, respectively. When tested on EEG from Model IV which was not used in developing the Epi-AI approach, Epi-AI was able to identify seizures with 76.3% sensitivity and 98.1% specificity. Significance. Epi-AI has the potential to provide fast, objective and reproducible semi-automated analysis of multiple types of seizure in long-duration EEG recordings in rodents.

Minimally invasive, automated cot-side tools for monitoring early neurological development can be used to guide individual treatment and benchmark novel interventional studies. We develop an automated estimate of the EEG maturational age (EMA) for application to serial recordings in preterm infants. The EMA estimate was based on a combination of 23 computational features estimated from both the full EEG recording and a period of low EEG activity (46 features in total). The combination function (support vector regression) was trained using 101 serial EEG recordings from 39 preterm infants with a gestational age less than 28 weeks and normal neurodevelopmental outcome at 12 months of age. EEG recordings were performed from 24 to 38 weeks post-menstrual age (PMA). The correlation between the EMA and the clinically determined PMA at the time of EEG recording was 0.936 (95%CI: 0.932–0.976; n = 39). All infants had an increase in EMA between the first and last EEG recording and 57/62 (92%) of repeated measures within an infant had an increasing EMA with PMA of EEG recording. The EMA is a surrogate measure of age that can accurately determine brain maturation in preterm infants.

Background: Poor agreement between observers on whether an unusual event is a seizure drives the need for a specific diagnostic tool provided by video-electroencephalography (video-EEG) in human pediatric epileptology. Objective: That successful classification of events would be positively associated with increasing EEG recording length and higher event frequency reported before video-EEG evaluation; that a novel wireless video-EEG technique would clarify whether unusual behavioral events were seizures in unsedated dogs. Animals: Eighty-one client-owned dogs of various breeds undergoing investigation of unusual behavioral events at 4 institutions. Methods: Retrospective case series: evaluation of wireless video-EEG recordings in unsedated dogs performed at 4 institutions. Results: Electroencephalography achieved/excluded diagnosis of epilepsy in 58 dogs (72%); 25 dogs confirmed with epileptic seizures based on ictal/interictal epileptiform discharges, and 33 dogs with no EEG abnormalities associated with their target events. As reported frequency of the target events decreased (annually, monthly, weekly, daily, hourly, minutes, seconds), EEG was less likely to achieve diagnosis (P

Ocular artifacts (OAs) are one of the major interferences that obscure electroencephalogram (EEG) signals. In this paper, a novel, completely automatic, adaptive and fast method that combines the Complete Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Renyi's Entropy (RE) is proposed for minimizing the OAs from corrupted EEG signals. The RE criterion is suggested to automatically select the Intrinsic Mode Functions (IMFs) to reconstruct the artifact minimized EEG signals. The scheme requires only a single channel OAs corrupted EEG recording and a reasonable computation time. The method is first evaluated on simulated OAs (one, two, and several blinks as well as saccadic eye movements) corrupted EEG signals and then extended to real EEG signals. The signal-to-noise ratio improvement (SNRimp) along with time and power spectral density (PSD) plots are used for evaluating the performance of the scheme. The method is compared to the one based on the CEEMDAN and manual choice of IMFs for OAs minimization from EEG. Results from extensive simulation studies clearly indicate the efficacy of the proposed scheme in automatically minimizing the OAs from the corrupted EEG signals.

Social neuroscience research investigating autism spectrum disorder (ASD) has yielded inconsistent findings, despite ASD being well-characterized by difficulties in social interaction and communication through behavioral observation. In particular, specific etiologies and functional and structural assays of the brain in autism have not been consistently identified. To date, most social neuroscience research has focused on a single person viewing static images. Research utilizing interactive social neuroscience featuring dual-brain recording offers great promise for the study of neurodevelopmental disabilities. Reward processing has been implicated in the pathology of ASD, yet mixed findings have brought uncertainty about the role reward processing deficits may play in ASD. The current study employed dual-brain EEG recording to examine reward processing during live interaction and its relation to autistic traits. Sixteen typically developing (TD) adults played a competitive treasure-hunt game against a computer and against a human partner. EEG results revealed enhanced neural sensitivity to reward outcome during live interaction with a human competitor. Further, individuals with higher levels of autistic traits demonstrated reduced sensitivity to reward outcome during live interaction. These findings provide novel insight into reward processing mechanisms associated with autistic traits, as well as support the necessary utility of interactive social neuroscience techniques to study developmental disorders.

This study was performed on sidewalks that there can secure movement of pedestrians. The basic theme of the research is how pedestrians are influenced by the sudden hearings of horns in the walking moments. In this study on different horns chord samples were randomly presented during simultaneous Electroencephalogram (EEG) recordings to a total of 48 healthy participants aged between 25–65 years; the elicited brain responses were examined. EEG recording with visual stimuli is performed on 48 subjects at delta, theta, alpha, beta and gamma frequency bands by using 5 different horn sounds which are selected from the real traffic environments. Especially trucks with a high-end horn sound, show very significant differences in the brainwaves of the pedestrians. Therefore, it is very important for pedestrians to make legal arrangements related to the voice of the high horn, especially in the developed countries.

Introduction: Occipital lobe epilepsies are rarely met in clinical practice, but when they occur, they can be misdiagnosed as migraine-like headache. Their prevalence ranges from 5%to 10% of all epilepsies. Seizures can occur at any age; etiologically speaking they can be symptomatic, cryptogenic and idiopathic (most often onsetis in childhood). Clinical symptomatology is manifested by partial epileptic seizures in the sense of visual elementary and/or complex manifestations, palinopsia, amaurosis, tonic head deviation, bulbus, nistagmus and headache. Propagation discharge to neighbour areas (temporal, parietal and frontal) is a frequent occurrence appearing with complex partial seizures frequently finishing with secondary generalized tonic-clonic (GTC) seizures. Case report: We are presenting a17-year-old male patient who has suffered from attacks of visual problemswith headache since 10 years of age. All the time it is treated as a migraine headache. During the last attack of headache the patient also had a loss of consciousness, EEG that was performed for the first time evidenced epileptic discharges of the occipital area. The therapy also included treatment with antiepileptic drug pregabalin resulting in seizure withdrawal. Conclusion: The appearance of visual symptoms followed by headache is most frequently qualified as migraine triggered headache. However, when antimigraine therapy does not give favorable results epileptic headache should be suspected, with obligatory performance of EEG recording. Occipital lobe epilepsy often presents diagnostic dilemmas due to clinical manifestations that are similar to that of non-migraine headache.

Electroencephalography (EEG) recordings are often contaminated by muscle artefacts. To address the problem, various methods have been proposed to suppress muscle artefacts from either multichannel or single-channel EEG recordings. However, there exist few studies for muscle artefact removal from few-channel EEG recordings. An effective solution for the few-channel situation by combining multivariate empirical mode decomposition (MEMD) with independent vector analysis (IVA), termed as MEMD-IVA, is proposed. The proposed method consists of two steps. MEMD is first utilised to decompose a few-channel EEG recording into intrinsic mode functions (IMFs) and then IVA is applied on the IMFs to separate sources related to muscle activity. The performance of the proposed method on simulated and real-life data is evaluated. The results demonstrated that MEMD-IVA outperforms other possible existing methods in a few-channel situation.

Electroencephalography (EEG) provides no direct link between electrode positions and underlying signal generators. Inferences based on spatial proximity between scalp positions and cortical structures are not reliable. More accurate source localization is obtained by solving both the forward and the inverse problem, but is technically challenging. In this paper, we provide a reference table of correspondence between EEG sensors and cortical anatomical regions based on a realistic head model. We also present a universal algorithm to compute the solution by using a forward model to determine the sensitivity for electrodes of any defined electrode positioning system and cortical anatomical parcellation.

Rodent electroencephalography (EEG) in preclinical research is frequently conducted in behaving animals. However, the difficulty inherent in identifying EEG epochs associated with a particular behavior or cue is a significant obstacle to more efficient analysis. In this paper we highlight a new solution, using infrared event stamping to accurately synchronize EEG, recorded from superficial sites above the hippocampus and prefrontal cortex, with video motion tracking data in a transgenic Alzheimer’s disease (AD) mouse model. Epochs capturing specific behaviors were automatically identified and extracted prior to further analysis. This was achieved by the novel design of a ultra- miniature wearable EEG recorder, the NAT-1 device, and its in- situ IR recording module. The device is described in detail, and its contribution to enabling new neuroscience is demonstrated.

The spatial mapping of localized events in brain activity critically depends on the correct identification of the pattern signatures associated with those events. For instance, in the context of epilepsy research, a number of different electrophysiological patterns have been associated with epileptogenic activity. Motivated by the need to define automated seizure focus detectors, we propose a novel data-driven algorithm for the spatial identification of localized events that is based on the following rationale: the distribution of emerging oscillations during confined events across all recording sites is highly non-uniform and can be mapped using a spatial entropy function. By applying this principle to EEG recording obtained from 67 distinct seizure epochs, our method successfully identified the seizure focus on a group of ten drug-resistant temporal lobe epilepsy patients (average sensitivity: 0.94, average specificity: 0.90) together with its characteristic electrophysiological pattern signature. Cross-validation of the method outputs with postresective information revealed the consistency of our findings in long follow-up seizure-free patients. Overall, our methodology provides a reliable computational procedure that might be used as in both experimental and clinical domains to identify the neural populations undergoing an emerging functional or pathological transition.Conflict of interestsThe authors declare no competing financial interests.

The assessment of personality is crucial not only for scientific inquiries but also for real-world applications such as personnel selection. In this study, we propose and validate a novel implicit measure to predict an individual's levels in the Big Five personality traits from 5 minutes of electroencephalography (EEG) recordings. Participants viewed Chinese words with positive, negative, and neutral emotions. The multi-channel event-related potentials elicited by these emotional words were used to train a sparse regression model for personality prediction. Results from a large test sample of 196 participants indicated that the personality scores derived from the proposed measure reached significant correlations with a commonly used questionnaire (r = .50, .60, .49, .55, and .49 for agreeableness, conscientiousness, neuroticism, openness, and extraversion, respectively). The EEG-based personality scores showed good external validity as well, capable of predicting behavioral indices and psychological adjustment similar to self-reported scores. Besides, the EEG-based scores were relatively stable across time, as reflected by the test-retest reliability of .5 ~ .7 for the five personality traits within a cohort of 33 participants 19-78 days later. These evaluations suggest that the proposed measure can serve as a viable alternative to conventional personality questionnaires in practice.

Objective To investigate whether the occurrence and morphology of interictal epileptiform discharges (IEDs) in scalp-EEG change by age. Methods 10,547 patients who had a standard or sleep deprived EEG recording reported using the SCORE standard were included. 875 patients had at least one EEG with focal IEDs. Focal IED morphology was analyzed by age using quantitative measures in EEGLAB and by visual classification based on the SCORE standard. We present distributions of IED measures by age group, with medians, interquartiles, 5th and 95th percentiles. Results Focal IEDs occurred most frequently in children and elderly. IED morphology and localization depended on age (p

The ability to mentally simulate an action by recalling the body sensations relative to the real execution is referred to as kinesthetic motor imagery (MI). Frontal and parietal motor-related brain regions are generally engaged during MI. The present study aimed to investigate the time course and neural correlates of complex action imagery and possible effects of expertise on the underlying action representation processes. Professional ballet dancers and controls were presented with effortful and effortless ballet steps and instructed to mentally reproduce each movement during EEG recording. Time-locked MI was associated with an Anterior Negativity (AN) component (400-550ms) that was larger in dancers relative to controls. The AN was differentially modulated by the motor content (effort) as a function of ballet expertise. It was more negative in response to effortful (than effortless) movements in control participants only. This effect also had a frontal distribution in controls and a centro-parietal distribution in dancers, as shown by the topographic maps of the scalp voltage. The source reconstruction (swLORETA) of the recorded potentials in the AN time-window showed enhanced engagement of prefrontal regions in controls (BA 10/47) relative to dancers, and occipitotemporal (BA 20) and bilateral sensorimotor areas in dancers (BA6/40) compared with controls. This evidence seems to suggest that kinesthetic MI of complex action relied on visuomotor simulation processes in participants with acquired dance expertise. Simultaneously, increased cognitive demands occurred in participants lacking in motor knowledge with the specific action. Hence, professional dance training may lead to refined action representation processes.