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Проведена апробация изготовленного по материалам открытого проекта шестнадцатиканального мобильного комплекса регистрации электроэнцефалограммы (ЭЭГ). Аппаратно-программный комплекс регистрации ЭЭГ позволяет проводить регистрацию неинвазивным способом 16-ти монополярных ЭЭГ каналов, содержащих биоэлектрические сигналы головного мозга человека. Все составные элементы комплекса регистрации конструктивно расположены на шлеме-основе из твердого пластика. Шлем надевается на голову и удерживает на себе до 32-х вкручивающихся штырьковых электродов, платы электронного устройства регистрации и обработки сигналов, радиопередатчики, аккумуляторные батареи. Регистрируемые сигналы ЭЭГ в режиме реального времени передаются по радиоканалу (стандарт Wi-Fi) на ЭВМ для последующей обработки. Сигналы ЭЭГ, полученные в ЭВМ, подаются в пакет прикладных программ MATLAB для последующей обработки. Сигналы ЭЭГ в ЭВМ формируются в виде стандартных цифровых отсчетов и, соответственно, могут быть переданы в любую программу обработки данных. Сигналы ЭЭГ должны быть подвергнуты математической обработке для выявления определенных состояний головного мозга и формирования паттернов ЭЭГ, служащих ориентирами при подготовке управляющих сигналов на внешние исполнительные устройства. При математической обработке полученных сигналов был проведен анализ частотного состава ЭЭГ, проведены специальные преобразования сигналов и вспомогательные операции для идентификации необходимых паттернов ЭЭГ сигналов. В первую очередь была проведена фильтрация полученных сигналов полосовым фильтром и алгебраической функцией вейвлета Добеши 8-го уровня. Затем были собраны контрольные образцы мозговой деятельности при выполнении трех типов активностей. Обнаружена корреляция между экспериментами и контрольными образцами. Сделанные наработки могут быть использованы для упрощения установки входных параметров искусственных нейронных сетей, применяемых для обработки и анализа сигналов ЭЭГ We carried out the approbation of a sixteen-channel mobile EEG registration complex made based on the materials of an open project. The hardware and software complex for EEG registration allows for non-invasive registration of 16 monopolar EEG channels containing bioelectric signals of the human brain. All the components of the registration complex are structurally located on a helmet-based made of hard plastic. The helmet is put on the head and holds up to 32 screw-in pin electrodes, boards of an electronic device for recording and processing signals, radio transmitters, and batteries. The recorded EEG signals are transmitted in real time via a radio channel (Wi-Fi standard) on a computer for subsequent processing. The EEG signals received in the computer are fed into the MATLAB application software package for subsequent processing. The EEG signals in the computer are formed in the form of standard digital samples and, accordingly, can be transmitted to any data processing program. EEG signals should be subjected to mathematical processing to identify certain states of the brain and form EEG patterns that serve as guidelines for the preparation of control signals to external actuators. During the mathematical processing of the received signals, we analyzed the frequency composition of the EEG, special signal transformations and performed auxiliary operations to identify the necessary EEG signal patterns. First of all, we filtered the received signals by a bandpass filter and an algebraic function of the Daubechy wavelet of the 8th level. Then, we collected control samples of brain activity when performing three types of activities. We found a correlation between the experiments and the control samples. It can be developed to be used to simplify the installation of input parameters of artificial neural networks used for processing and analyzing EEG signals

Removing different types of artifact from the electroencephalography (EEG) recordings is a critical step in performing EEG signal analysis and diagnosis. Most of the existing algorithms aim for removing single type of artifact, leading to a complex system if an EEG recording contains different types of artifact. With the advancement in wearable technologies, it is necessary to develop an energy efficient algorithm to deal with different types of artifact for single-channel wearable EEG devices. In this paper, an automatic EEG artifact removal algorithm is proposed that effectively reduces 3 types of artifact, i.e., ocular blink artifact (OA), transmission-line/harmonic wave artifact (TA/HA), and muscle artifact (MA), from a single-channel EEG recording. The effectiveness of the proposed algorithm is verified on both simulated noisy EEG signals and real EEG from CHB-MIT dataset. The experimental results show that the proposed algorithm effectively suppresses OA, MA, TA/HA from a single EEG channel record as well as physical movement artifact.

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.

The occipital face area (OFA) is hierarchically one of the first stages of the face processing network. It has originally been thought to be involved in early, structural processing steps, but currently more and more studies challenge this view and propose that it also takes part in higher level face processing, such as identification and recognition. Here we tested whether the OFA is involved in the initial steps of recognition memory and plays a causal role in the differential processing of familiar and unfamiliar faces. We used an offline, inhibitory continuous theta-burst stimulation (cTBS) protocol over the right OFA and the vertex as control site. Electroencephalographic (EEG) recording of event-related potentials (ERPs), elicited by visually presented familiar (famous) and unfamiliar faces was performed before and after stimulation. We observed a difference in ERPs for famous and unfamiliar faces in a time-window corresponding to the N250 component. Importantly, this difference was significantly increased by cTBS of the right OFA, suggesting its causal role in the differential processing of familiar and unfamiliar faces. The enhancement occurred focally, at electrodes close to the right hemispheric cTBS site, as well as over similar occipito-temporal sites of the contralateral hemisphere. To the best of our knowledge, this is the first study showing the causal role of the rOFA in the differential processing of familiar and unfamiliar faces, using combined cTBS and EEG recording methods. These results are discussed with respect to the nature of familiar face representations, supported by an extensive, bilateral network.

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

SUMMARY Generalized periodic discharges with triphasic morphology were previously referred to as triphasic waves but have now been subsumed into the ACNS classification as generalized periodic discharges. Although triphasic waves and generalized spike-wave complexes may resemble each other and hence may be incorrectly identified in comatose critically ill patients, many authors believe that there are different entities, with definable morphologic and clinical differences attributable to each waveform. The occurrence of both patterns in the same patient is extremely rare with only a single prior case report. Here the authors report a patient with typical triphasic waves and generalized spike-wave complexes and highlight the morphologic and EEG differences between the two patterns. The occurrence of both waveforms in the same EEG recording supports the notion of different cerebral generators and pathways, further differentiating rather than merging these morphologies.

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.

We examined times (latencies) of two types of the sensorimotor (visuomotor) reactions, simple sensorimotor reaction (SSMR) and choice motor reaction (ChMR), in men (combatants) suffering from the consequences of mild traumatic brain injury (mTBI) during the Joint Forces Operation in the East of Ukraine (17 right-handed volunteers aged 27–43 years) and compared the respective values with those demonstrated by healthy control men (16 right-handed volunteers, 18–21 years). Combatants with mTBI were characterized by significantly greater values of the latency in the ChMR, mostly determined by considerably longer times necessary for decision-making processes. Results of the analysis of cerebral network connectivity based on the data of EEG recording, coherence measurements, and LORETA neurovisualization gave reasons to suppose that a large-scale synchronized frontoparietal neuronetwork functions in control subjects; this network is responsible for decision making according to the results of control, analysis of information, and coordination of various alternatives. Unlike this, an associative decision-making system mostly functions in combatants with mTBI; this system uses coding networks in the primary and secondary visual cortices; functioning of the above associative system is followed by the creation of imaginary phenomena and associations, which are then realized in motor acts.

Epilepsy is a neural disorder that is associated with the central nervous system (CNS) in which the brain activity sometimes becomes abnormal, which may lead to seizures, loss of awareness, unusual sensations, and behavior. Electroencephalograms (EEG) are widely used to detect epilepsy accurately. However, the interpretation of a particular type of abnormality using the EEG signal is a subjective affair and may vary from clinician-to-clinician. Visual inspection of the EEG signal by observing a change in frequency or amplitude in long-duration signals is an arduous task for the clinicians. It may lead to an erroneous classification of EEGs. The proposed methodology focuses on automated detection of epilepsy using a novel stop-band energy (SBE) minimized orthogonal wavelet filter bank. Using the wavelet decomposition, we obtain subbands (SBs) of EEG signals. Subsequently, fuzzy entropy, logarithmic of the squared norm, and fractal dimension are computed for each SB. The different combinations of the extracted features were supplied to various classifiers for the classification of normal and abnormal EEG signals. In the proposed method, we have used a single-channel EEG dataset of Temple University Hospital. The dataset is the most substantial EEG data publicly available, which contains an EEG recording of 2130 distinct subjects. Our proposed system obtained the highest classification accuracy (CACC) of 78.4% and 79.34% during training and evaluation using the SVM classifier. We achieved the highest F1-score of 0.88.

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.

The aim of this study was to develop a novel dry electroencephalography (EEG) sensor with a soft, pliable pad that conforms to the contours of the skin and skull, providing a suitable surface contact area for collecting electrical potential signals and ensuring a reliable connection. In this study, based on our experience in developing flexible silver/silicon-based dry-contact sensors (SBDSs) for biosignal measurements, we proposed a new, augmented wire-embedded silicon-based dry-contact sensor (WSBDS) with a long lifespan and better performance in EEG measurements. The following two augmentation concepts were proposed in this design and implemented in fabrication: 1) the addition of a metal stud and 2) the embedding of copper wires into the fingers of an acicular SBDS. The forehead sensor is suitable for forehead EEG measurements, and the acicular sensor is designed for application to hair-covered sites, where it can overcome hair interference to achieve satisfactory scalp contact while maintaining low impedance at the skin-electrode interface. Finally, this augmented WSBDS performed well in human EEG recording in a designed brain-computer interface (BCI) experiment and is feasible for practical applications.

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

Human electroencephalogram (EEG) biofeedback (neurofeedback) started in the 1940s using one EEG recording channel, then four channels in the 1990s, and in 2004, expanded to 19 channels using Low Resolution Electromagnetic Tomography (LORETA) of the microampere three-dimensional current sources of the EEG. In 2004–2006 the concept of a real-time comparison of the EEG to a healthy reference database was developed and tested using surface EEG z score neurofeedback based on a statistical bell curve called real-time z scores. The real-time or live normative reference database comparison was developed to help reduce the uncertainty of what threshold to select to activate a feedback signal and to unify all EEG measures to a single value (i.e., the distance from the mean of an age-matched reference sample). In 2009 LORETA z score neurofeedback further increased specificity by targeting brain network hubs referred to as Brodmann areas. A symptom checklist program to help link symptoms to dysregulation of brain networks based on fMRI and positron emission tomography (PET) and neurology was created in 2009. The symptom checklist and National Institutes of Health–based networks linking symptoms to brain networks grew out of the human brain mapping program started in 1990 that continues today. A goal is to increase specificity of EEG biofeedback by targeting brain network hubs and connections between hubs likely linked to the patient's symptoms. Developments first introduced in 2017 provide increased resolution of three-dimensional source localization with 12,700 voxels using swLORETA with the capacity to conduct cerebellar neurofeedback and neurofeedback of subcortical brain hubs such as the thalamus, amygdala, and habenula. Future applications of swLORETA z score neurofeedback represent another example of the transfer of knowledge gained by the human brain mapping initiatives to further aid in helping people with cognition problems as well as balance problems and parkinsonism. A brief review of the past, present, and future predictions of z score neurofeedback are discussed with special emphasis on new developments that point toward a bright and enlightened future in the field of EEG biofeedback.

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.

Time-shared performance asymmetry in right-handed subjects is attributed to left hemisphere lateralization of both speech and motor control of the contralateral right hand. The present study investigated patterns of time-shared motor performance and EEG asymmetry in mentally retarded individuals. Thirty-six institutionalized mentally retarded individuals served as subjects. All subjects had previously come to the laboratory for an EEG recording and were known to be cooperative. Analysis of variance of tapping scores showed that there were no significant differences between left- and right-handed subjects; hence, the data for all subjects are pooled in the analyses that follow. In conclusion, the assessment of baseline hemispheric EEG patterns is seen to correlate significantly with interference during time-shared motor performance in a sample of institutionalized mentally retarded individuals. It is possible that such assessments may prove helpful in prescribing sensorimotor remediation. .

In a recent study, we found that during 20.55 ± 1.60 h of artifact-free ambulatory EEG recordings, epileptiform discharges (EDs) longer than 2.68 s occurred exclusively in patients with Juvenile Myoclonic Epilepsy (JME) who experienced seizure recurrence within a year after the EEG. Here we expanded this analysis, exploring whether long EDs (2.68 s), and short ones, were uniformly distributed during the day. Lastly, we evaluated the temporal distribution of seizure relapses. By Friedman test, we demonstrated that hourly frequencies of both short and long EDs were dependent on the hours of day and sleep-wake cycle factors, with an opposite trend. Short EDs were found mostly during the night (with two peaks at 1 AM and 6 AM), and sleep, dropping at the wake onset (p 0.001). Conversely, long EDs surged at the wake onset (0.001), remaining frequent during the whole wake period, when compared to sleep (p = 0.002). Of note, this latter pattern mirrored that of seizures, which occurred exclusively during the wake period, and in 9 out of 13 cases at the wake onset. We therefore suggested that short and long EDs could reflect distinct pathophysiological phenomena. Extended wake EEG recordings, possibly including the awakening, could be extremely useful in clinical practice, as well as in further studies, with the ambitious goal of predicting seizure recurrences.

Objectives. To confirm the authors’ previous suggestion that mutations in the Panx1 gene (pannexin-1) may play an important role in the development of impairments to the sleep-waking cycle. Materials and methods. Studies were carried out using mice with knockout of the Panx1 gene. Experiments used continuous 24-h EEG recording of the EEG and motor activity. Results and conclusions. A statistically significant increase in the proportion of waking was found, due to a decrease in the slow-wave sleep phase (as compared with controls), particularly marked in the dark period of the day, along with an increase in motor activity. The results were compared with data obtained during investigation of a recently published case of a patient with a homozygous mutation in this gene. The possible physiological role of pannexin-1 protein in health and pathology is discussed.

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.

An approach to find the best patient-specific seizure detection system for nocturnal frontal lobe epilepsy (NFLE) is proposed. Data of several patients affected by NFLE, extracted from the European Epilepsy Database, have been used for this study. As every patient possesses different physiological characteristics, several simulations were performed in order to find the best features to be extracted from the best number of electroencephalogram (EEG) channels and to be inputted to two-dimensional self-organizing maps (SOM). The proposed method allows the definition of maps capable of presenting meaningful information on the actual brain state, revealing the mapping potential of clustering the data coming from seizure and non-seizure patterns; moreover, it also suggests to use SOM for early seizure detection. In fact, the temporal sequence of the patterns in an EEG recording can be projected on the SOM, obtaining a trajectory that describes the dynamics of the brain state as captured by the EEG. The analysis of this trajectory allows one to detect the seizure event, highlighting the possibility to promote therapies aimed at rapid and targeted disarming the seizures. A further contribution of the paper is to propose a methodology to be followed to customize the prediction model to a new patient. The performances of the proposed approach, in terms of sensitivity and specificity, have been compared with those of other well-established detection models proving it is a good alternative to early and accurately detect nocturnal frontal lobe epilepsy seizures.

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.

With the rapid development of brain–computer interfaces (BCI), the number of applications that use BCI technology is increasingly thick and fast. Prediction of age and gender of a person through EEG analysis is a new application of BCI that has been proposed in this paper. An industry standard EEG recording device has been used to record cerebral activities of 60 subjects (both male and female) in relaxed position with closed eyes. Deep BLSTM-LSTM network has been used to construct a hybrid learning framework for the aforementioned analysis. Accuracy of 93.7% and 97.5% have been recorded for age and gender classification problems respectively. These values are better than the state-of-the-art methods. Our analysis also reveals that the beta band frequencies are better in predicting the age and gender as compared to other frequency bands of the EEG signals. The proposed method has several applications, including biometric, health-care, entertainment, and targeted advertisements.

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.

Synchronous fMRI-EEG mapping of cerebral activity in stroke patients made it possible to implement neurofeedback, a novel and promising therapeutic technology. This method integrates a real-time monitoring of cerebral activity by EEG and fMRI signals and training of the patients to control this activity simultaneously or alternatively via neurofeedback. The targets of such cerebral stimulation are cortical regions controlling arbitrary movements (Brodmann area 4), whereas its aim is optimization of activity in these regions in order to achieve better rehabilitation of stroke patients. The paper discusses the methodical details, advantages, and promise of bimodal neurofeedback treatment.

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.

SUMMARY Recording of interictal epileptiform discharges to classify the epilepsy syndrome is one of the most common indications for ambulatory EEG. Ambulatory EEG has superior sampling compared with standard EEG recordings and advantages in terms of cost-effectiveness and convenience compared with a prolonged inpatient EEG study. Ambulatory EEG allows for EEG recording in all sleep stages and transitional states, which can be very helpful in capturing interictal epileptiform discharges. In the absence of interictal epileptiform discharges or in patients with atypical events, the characterization of an epilepsy syndrome may require recording of the habitual events. Diagnostic ambulatory EEG can be a useful alternative to inpatient video-EEG monitoring in a selected number of patients with frequent events who do not require medication taper or seizure testing for surgical localization.

There is an ongoing discussion in the NeuroIS (Neuro-Information-Systems) discipline on whether consumer-grade EEG instruments are as suitable for scientific research as research-grade instruments. Considering the increasing adoption of consumer-grade instruments along with the fact that many NeuroIS EEG papers used such tools, this debate is fundamental. We report on a study in which we contrasted a 61-channel EEG recording with a 14-channel recording that should simulate the electrode layout of the EPOC headset, the presumably worldwide most widely used consumer-grade tool. The contrast was carried out based on topographic mapping, because this kind of EEG data analysis does not only play a significant role in cognitive neuroscience, but also in NeuroIS research. Our findings show noticeable differences in the topoplots between both conditions. The current research results are limited by the fact that our task context is a non-IS context (i.e., upper limb movements). Hence, future research should validate our results based on IS tasks and situations in order to confirm, revise, or falsify the present results.

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.

Motion artifacts are arguably the most important issue in the development of wearable ambulatory EEG devices. Designing circuits and systems capable of high-quality EEG recording regardless of these artifacts requires a clear understanding of how the electrode-skin interface is affected by physical motions. In this work, first, we report statistically-significant experimental characterization results of electrodeskin interface impedance for dry contact and non-contact electrodes in the presence of various motions. This leads to a model describing the motion-induced electrode-skin interface impedance variations for these electrodes. Next, a critical review of the possible analog front-end circuits for surface EEG recording is presented, followed by theoretical circuit analysis discussing the effect of electrode movements on the operation of these circuits. Inspired by the developed model and the analytical review, a novel front-end architecture capable of extracting motion from the EEG signal during the amplification stage is presented and experimentally characterized.

Trauma exposure is prevalent, associated with multiple forms of psychopathology, and thought to alter the neurobiological substrates of threat processing. The late positive potential (LPP) is an event-related potential (ERP) that may be a clinically useful probe of the neurobiology of threat processing. Despite evidence that combat-exposed veterans exhibit aberrant threat modulation of the LPP, no studies to date have tested the psychometric properties of the LPP in combat trauma-exposed, symptomatic veterans. The primary aim of the current study was to evaluate the reliability (internal consistency, retest reliability) and convergent validity of LPP modulation by threatening faces and scenes in two common tasks among combat-exposed veterans. Participants included 82 combat-exposed veterans who completed face-matching and emotion regulation tasks during EEG recording at baseline and twelve weeks. Internal consistencies of the early LPP time windows (1000 ms) were acceptable in both tasks, whereas they were poor in late time windows (1000 ms). Twelve-week retest reliabilities were fair for the early window LPPs to threatening scenes and fear faces, as well as in the late time window for fear faces. Reliabilities were better for individual condition compared to difference scores. Finally, LPPs modulated by threatening scenes and faces were unrelated. Together, these results suggest that the LPPs to threatening scenes and faces reflect distinct forms of threat processing in combat-exposed veterans, and their reliabilities for the early window indicate potential clinical utility in this population.

Independent Component Analysis (ICA) has became the most popular method to remove eye-blinking artifacts from electroencephalogram (EEG) recording. For long term EEG recording, ICA was commonly considered to costing a lot of computation time. Furthermore, with no ground truth, the discussion about the quality of ICA decomposition in a nonstationary environment was specious. In this study, we investigated the "signal" (P300 waveform) and the "noise" (averaged eye-blinking artifacts) on a cross-modal long-term EEG recording to evaluate the efficiency and effectiveness of different methods on ICA eye-blinking artifacts removal. As a result, it was found that, firstly, down sampling is an effective way to reduce the computation time in ICA. Appropriate down sampling ratio could speed up ICA computation 200 times and keep the decomposition performance stable, in which the computation time of ICA decomposition on a 2800 s EEG recording was less than 5 s. Secondly, dimension reduction by PCA was also a way to improve the efficiency and effectiveness of ICA. Finally, the comparison by cropping the dataset indicated that performing ICA on each run of the experiment separately would achieve a better result for eye-blinking artifacts removal than using all the EEG data input for ICA.

The goal of this paper is to develop a Brain Computer Interface (BCI) based on voluntary eye blinks decoding. In particular, the study was focused on the signals generated in the cortex by eye blinking, which can be collected by frontopolar scalp Electroencephalographic (EEG) sensors. Normally, EEG recording systems meant for clinical applications are expensive and cannot be used in large-scale user-friendly applications. Thanks to a prototype made by the STMicroelectronics company, based on an Open Source EEG project, a low-cost EEG recording system was created in this work. The goal is to develop an algorithm that can detect and discriminate between voluntary (forced) and involuntary (natural) blinking so that, in the future, an EEG-based BCI system that is able to control a device through eye movements could be developed, which would be of great use for all people with motor disabilities who can control eye movements. The proposed algorithm is based on a one-dimensional (1D) Convolutional Neural Network (CNN) architecture. Frontopolar EEG signals were collected during the execution of voluntary and spontaneous blinks by four healthy subjects. A dataset of EEG epochs of including blinks was constructed and used to train and validate the proposed CNN. The proposed system allowed to discriminate the blinks performed by the subjects (voluntary vs. involuntary) with an average accuracy of 97.92%.

Aim of this study was to measure possible hypnotizability related differences in the amplitude of cortical heartbeat evoked potentials (HEP) measured during nocturnal sleep stages. Polysomnographic EEG recording was performed in subjects with high (highs, n=6), medium (mediums, n=8) and low (lows, n=24) hypnotizability scores. The mean amplitude of the HEPs for each electrode and sleep stage was calculated in the time window from 200 ms to 600 ms after the R peaks. A multivariate ANOVA using hypnotizability as between-subjects factor was performed for each sleep stage. Results showed higher HEPs mean amplitude in mediums and highs with respect to lows in central, parietal and occipital right regions, during N2, N3 and REM sleep stages. We can hypothesize that, during sleep, individuals with medium-to-high hypnotizability maintain a higher responsiveness to interoceptive information - that is greater body awareness - with respect to lows.

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.

Background Nonconvulsive status epilepticus (NCSE) requires an EEG for diagnosis and in many centers access may be limited. The authors aimed to test whether neurology residents can be trained to use and interpret full-montage EEGs using an EEG cap electrode system to detect NCSE while on-call. Methods Neurology residents were trained to interpret EEG recordings using the American Clinical Neurophysiology Society critical care EEG terminology. Residents who achieved a score of 70% or higher in the American Clinical Neurophysiology Society certification test and attended a training session were eligible to use the EEG cap on-call with patients suspected of having NCSE. Residents' experience and interpretation of observed EEG patterns were evaluated using a questionnaire. Each EEG recording was independently reviewed by three epilepsy specialists to determine the interpretability of each study and whether the residents correctly identified the EEG patterns. Results Sixteen residents undertook the training and 12 (75%) achieved a score of 70% or higher on the certification test. Seven of these residents performed 14 EEG cap studies between August 2017 and May 2018. The percent agreement between residents and electroencephalographers was 78.6% for EEG interpretability and 57.1% for description of EEG pattern. Residents did not miss any malignant patterns concerning for NCSE, which accounted for 1 of 14 EEGs but "overcalled" patterns as malignant in 3 of 14 recordings. Conclusions This study suggests that neurology residents can be taught to perform and interpret EEGs using a cap system to monitor for NCSE. Additional training will help improve EEG interpretation and sensitivity.

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).

Epilepsy is the most rampant neurological disorder that is diagnosed for around 50 million people in the world. We obtain beneficial information related to the different physiological states of the brain via the electroencephalogram (EEG). S pikes in an EEG recording serves as a major parameter for diagnosing epilepsy in an individual [2]. This research aims to efficiently identify and classify Spikes in an EEG Signal using multilayer filtration of noises to predict epilepsy. The methodology explained in the following research introduces two layers of a classifier to classify time series into spikes and nonspikes given a channel of EEG recording. The advantages of the proposed method are its computational simplicity, easy algorithmic implementation, and immunity to artifacts, noise, and implicit characteristics of EEG. As opposed to a naive approach of sliding windows for the classification of time series, the following method uses a layer of the preliminary classifier with very high classification sensitivity and a layer of classification, where the focus is given on high classification accuracy. The detection algorithm uses mathematical operations and thresholding in the first layer and uses the support vector machine(SVM) classifier in the second layer to classify the time series windows into spikes and non-spikes in an EEG recording.

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.

The capability model of anterior asymmetry integrates trait-related and state-related frontal asymmetry research by proposing that frontal asymmetry is dependent on relevant traits if they are activated by a situation. However, differences in experimental design and EEG recording methods haven’t been fully explored. We investigated 56 participants under three different situational paradigms (virtual T-maze, mental imagery, movies), varying the stimulus and type of measurement concerning frontal asymmetry. We predicted that “strong” situational manipulations (virtual T-maze, frontal asymmetry measured as event-related desynchronization) would eclipse relationships between frontal asymmetry and relevant traits, whereas “weaker” task manipulations, measured during longer time periods, would enhance relationships to relevant traits compared to frontal asymmetry at rest. The results confirmed these expectations, stressing the importance of stimulus characteristics, trait measures and recording methods with respect to the capability model. Additionally, a revision of the capability model to an inverse U-shaped quadratic relationship might be appropriate.

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.

In this paper, we develop a simulation platform for the Brain-Computer Interface (BCI) based smart wheelchair. The main contribution of our system is to implement an efficient simulation platform for the next generation wheelchair. Meanwhile, due to disabled people training with a real-natural environment that is difficult or costly to access, a means to synthetically recreate a virtual environment is necessary to assist patient training for the wheelchair driving. Emotiv neuroheadset provides an effective and reliable means of performing EEG testing during wheelchair training, which is inexpensive and delivers data of similar quality compared to other EEG recording equipment.

My 50 year contributions to EEG Biofeedback (Neurofeedback) started with animal studies in the 1960s – 1970s that was then applied to human subjects using 1 to 4 EEG recording channels in the 1970s until 2009 when I introduced 19 channel surface EEG Z score Neurofeedback. This was followed by LORETA Z score Neurofeedback in 2010 that increased specificity by targeting brain network hubs (Brodmann areas) and connections between hubs likely to the patient’s symptoms. Now in 2019 there are over 3,000 clinicians using Z score Neurofeedback with over 60 scientific publications. In 2019 I introduced swLORETA with 12,700 voxels with the capacity to conduct Cerebellar Neurofeedback and Neurofeedback of subcortical brain hubs such as the thalamus, amygdala and habenula with ongoing research in support of these new advances.

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.