Electrical Activity Of Human Brain Research Articles

N°24 – Chronic effects of Transcendental Meditation (TM) on the human brain electrical activity in professional firefighters: A LORETA study

N°24 – Chronic effects of Transcendental Meditation (TM) on the human brain electrical activity in professional firefighters: A LORETA study

The creation of the psychological equipment in Ukraine and Armenia by Yevhen Mileryan

The article reveals the content and significance of multifaceted psychological research of Yevhen Oleksandrovych Mileryan (1913-1983) – the creator of measuring, training, cognitive, experimental, control psychological equipment, numerous methods of psychological selection of pilots, operators, drivers, the use of which allowed to prevent accidents. caused by people involved in the management of technical systems of different levels of complexity. The main milestones of the life of the Ukrainian and Armenian scientist, who suffered the trials of the Second World War, the variability of periods of creative self-realization and repression, work in Ukraine and Armenia are presented. This, in particular, the establishment of experimental workshops for the manufacture of psychological devices at the Research Research Institute of Psychology of the Ministry of Education of the USSR and the creation of the first “Device for demonstrating optical sensations and perception of psychology”, which allowed to demonstrate patterns of color mixing, contrast the phenomenon of marginal contrast and saturation, later – an electron-beam device for observing the dynamic picture of the spatial distribution of electric potentials, on the basis of which changes in the activity of the cerebral cortex under the influence of various sound stimuli in the presence or absence of attention were established. electroconvulsive attack. Documentary testimonies and recollections of eyewitnesses about harassment and repressions against E.O. Mileryan, connected with the persecution of all Ukrainians in Ukraine in the early 70s of the XX century, are given. The period of the scientist’s work in Armenia is revealed, which includes continuation of psychophysiological experiments on studying attention in children, elucidation of functional changes of electrical activity of human brain under the influence of various factors, substantiation of psychological theory of general polytechnic skills formation. vocational education. The scientist created instrumental and psychological methods for diagnosing individual psychophysiological features of a person, diagnostic installations and simulators to study the professional suitability of operators, pilots, drivers. Developed the concept of engineering and psychological examination to assess the quality and reliability of the system “man-machine”, psychological devices: apparatus for the study of higher nervous activity, attentiometer, electroencephalometer, emokoordininometer, device KM-3 to measure the speed of response to complex signals, perceptometer, volute study of volitional efforts, thinking hysteroscope, complication apparatus, device for studying the professional suitability of drivers of vehicles, educational and problematic electrical engineering simulators. It is noted that the unique contribution of E.O.Mileryan to the development of psychological science is the hardware-psychological direction of combining scientific accuracy of measurements with creative solution of various psychological problems that posed to the military and postwar generations of the twentieth century.

An Approach of Brain-Computer Interface Electroencephalography for Measuring Visual Height Intolerance

The environment is one factor that influences the quality of life, including a high environment for people with the fear of height (Visual Height Intolerance, VHI). Currently, VHI is measured by using the Visual Height Intolerance Severity Scale (VHISS). The lack of evidence-based testing makes these measurements feel weak and less meaningful. The use of Virtual Reality (VR) and Electroencephalography (EEG) based on the Brain-Computer Interface (BCI) deserves to be tested. The test is done by reading the human brain's electrical activity using a BCI-based EEG when given VR exposure. The analysis process uses a simple wave concept. Furthermore, the correlation study was carried out using the Spearman-rho method with a consideration of the normality test, which produced non-parametric data. The correlation test results show that the BCI-based EEG biometric data in the form of the amount of waves per time and magnitude has a strong relationship with the VHISS scale. The higher the number of waves per time, the higher the amplitude, the higher the VHISS scale. The evaluation was carried out by examining the correlation based on the demographics of age and gender. Finally, EEG based on BCI and VR can be an alternative and concrete evidence to review the level of visual height intolerance other than VHISS.

A study of changes in cognitive evoked potentials in persons with visual impairment

In this work we have used psychophysiological assessments of the human brain electrical activity according to the classical neurological method for calculating the evoked potential. The experiment was designed to extraction cognitive evoked potentials. Taking into account the characteristic components, the temporal dynamics of the EEG data channels was investigated. This approach allows one to consistently assess the distribution of all components of the evoked potential on the subject's head map. Based on the results of evoked potentials processing, a statistical comparison of the components of evoked potentials in subjects of different groups by channels was carried out in accordance with the Wilcoxon test. Demonstrated for which channels the results significantly differ between the two groups of subjects. The sequence of evoked potential analysis demonstrated in the article suits for adjusting the settings of the “brain-computer” systems for a particular subject and allows to select channels used in further BCI training efficiently.

Non-Canonical Microstate Becomes Salient in High Density EEG During Propofol-Induced Altered States of Consciousness.

Dynamically assessing the level of consciousness is still challenging during anesthesia. With the help of Electroencephalography (EEG), the human brain electric activity can be noninvasively measured at high temporal resolution. Several typical quasi-stable states are introduced to represent the oscillation of the global scalp electric field. These so-called microstates reflect spatiotemporal dynamics of coherent neural activities and capture the switch of brain states within the millisecond range. In this study, the microstates of high-density EEG were extracted and investigated during propofol-induced transition of consciousness. To analyze microstates on the frequency domain, a novel microstate-wise spectral analysis was proposed by the means of multivariate empirical mode decomposition and Hilbert-Huang transform. During the transition of consciousness, a map with a posterior central maximum denoted as microstate F appeared and became salient. The current results indicated that the coverage, occurrence, and power of microstate F significantly increased in moderate sedation. The results also demonstrated that the transition of brain state from rest to sedation was accompanied by significant increase in mean energy of all frequency bands in microstate F. Combined with studies on the possible cortical sources of microstates, the findings reveal that non-canonical microstate F is highly associated with propofol-induced altered states of consciousness. The results may also support the inference that this distinct topography can be derived from canonical microstate C (anterior-posterior orientation). Finally, this study further develops pertinent methodology and extends possible applications of the EEG microstate during propofol-induced anesthesia.

Classifying electrical activity of the brain during imaginary movements of untrained subjects using artificial neural networks

Introduction: Developing new classification methods for human brain electrical activity patterns corresponding to actual movements or motor imagery is an essential interdisciplinary problem in brain-computer interface research. One of the most promising approaches is the development of methods based on artificial neural networks. Purpose: The development of ANN-based methods for classifying electroencephalographic patterns associated with motor imagery in untrained subjects. Methods: Classifiers based on linear neural networks, multi-layer perceptrons, radial basis function networks and support vector machines. Results: The authors selected the optimal type, topology, learning algorithms and parameters of an artificial neural network in order to provide the most accurate and fast classification of lower limb motor imagery EEG signals. It has been studied how the number of the analyzed channels of a multichannel EEG and their choice affect the quality of motor imagery patterns classification. Optimal configurations were obtained for the electrode arrangements. The influence of EEG pre-processing on the accuracy of motor imagery recognition was analyzed. A computational experiment showed the accuracy of 90-95% in untrained subjects. Radial basis function network demonstrated the best performance. Besides, the dataset dimensionality has been significantly reduced down to 6–12 channels without any classification accuracy loss. Practical relevance: The obtained results can be useful for the developers of motor imagery EEG classification algorithms used in brain-computer interfaces.

Deep C-LSTM Neural Network for Epileptic Seizure and Tumor Detection Using High-Dimension EEG Signals

Electroencephalography (EEG) is a common and significant tool for aiding in the diagnosis of epilepsy and studying the human brain electrical activity. Previously, the traditional machine learning (ML)-based classifier are used to identify the seizure by extracting features from the EEG signals manually. Although the effectiveness of these contributions have already been proved, they cannot achieve multiple class classification with automatic feature extraction. Meanwhile, the identifiable EEG segment is too long to limit the capability of real-time epileptic seizure detection. In this paper, a novel deep convolutional long short-term memory (C-LSTM) model is proposed for detecting seizure and tumor in human brain and identifying two eyes statuses (open and close). It achieves to predict a result in every 0.006 seconds with a short detection duration (one second). By comparing with other two types deep learning approaches (DCNN and LSTM), the presented deep C-LSTM obtains the best performance for classifying these five classes. All of the obtained total accuracy are over 98.80%.

The EEG microstate topography is predominantly determined by intracortical sources in the alpha band

Human brain electric activity can be measured at high temporal and fairly good spatial resolution via electroencephalography (EEG). The EEG microstate analysis is an increasingly popular method used to investigate this activity at a millisecond resolution by segmenting it into quasi-stable states of approximately 100 ms duration. These so-called EEG microstates were postulated to represent atoms of thoughts and emotions and can be classified into four classes of topographies A through D, which explain up to 90% of the variance of continuous EEG. The present study investigated whether these topographies are primarily driven by alpha activity originating from the posterior cingulate cortex (all topographies), left and right posterior cortices, and the anterior cingulate cortex (topographies A, B, and C, respectively).We analyzed two 64-channel resting state EEG datasets (N = 61 and N = 78) of healthy participants. Sources of head-surface signals were determined via exact low resolution electromagnetic tomography (eLORETA). The Hilbert transformation was applied to identify instantaneous source strength of four EEG frequency bands (delta through beta). These source strength values were averaged for each participant across time periods belonging to a particular microstate. For each dataset, these averages of the different microstate classes were compared for each voxel. Consistent differences across datasets were identified via a conjunction analysis.The intracortical strength and spatial distribution of alpha band activity mainly determined whether a head-surface topography of EEG microstate class A, B, C, or D was induced. EEG microstate class C was characterized by stronger alpha activity compared to all other classes in large portions of the cortex. Class A was associated with stronger left posterior alpha activity than classes B and D, and class B was associated with stronger right posterior alpha activity than A and D.Previous results indicated that EEG microstate dynamics reflect a fundamental mechanism of the human brain that is altered in different mental states in health and disease. They are characterized by systematic transitions between four head-surface topographies, the EEG microstate classes. Our results show that intra-cortical alpha oscillations, which likely reflect decreased cortical excitability, primarily account for the emergence of these classes. We suggest that microstate class dynamics reflect transitions between four global attractor states that are characterized by selective inhibition of specific intra-cortical regions.

A repetitive modular oscillation underlies human brain electric activity.

The modular function j, central in the assessment of abstract mathematical problems, describes elliptic, intertwined trajectories that move in the planes of both real and complex numbers. Recent clues suggest that the j-function might display a physical counterpart, equipped with a quantifiable real component and a hidden imaginary one, currently undetectable by our senses and instruments. Here we evaluate whether the real part of the modular function can be spotted in the electric activity of the human brain. We assessed EEGs from five healthy males, eyes-closed and resting state, and superimposed the electric traces with the bidimensional curves predicted by the j-function. We found that the two trajectories matched in more than 85% of cases, independent from the subtending electric rhythm and the electrode location. Therefore, the real part of the j-function's peculiar wave is ubiquitously endowed all over normal EEGs paths. We discuss the implications of such correlation in neuroscience and neurology, highlighting how the j-function might stand for the one of the basic oscillations of the brain, and how the still unexplored imaginary part might underlie several physiological and pathological nervous features.

Mapping the dynamic repertoire of the resting brain

The resting state dynamics of the brain shows robust features of spatiotemporal pattern formation but the actual nature of its time evolution remains unclear. Computational models propose specific state space organization which defines the dynamic repertoire of the resting brain. Nevertheless, methods devoted to the characterization of the organization of brain state space from empirical data still lack and thus preclude comparison of the hypothetical dynamical repertoire of the brain with the actual one. We propose here an algorithm based on set oriented approach of dynamical system to extract a coarse-grained organization of brain state space on the basis of EEG signals. We use it for comparing the organization of the state space of large-scale simulation of brain dynamics with actual brain dynamics of resting activity in healthy subjects. The dynamical skeleton obtained for both simulated brain dynamics and EEG data depicts similar structures. The skeleton comprised chains of macro-states that are compatible with current interpretations of brain functioning as series of metastable states. Moreover, macro-scale dynamics depicts correlation features that differentiate them from random dynamics. We here propose a procedure for the extraction and characterization of brain dynamics at a macro-scale level. It allows for the comparison between models of brain dynamics and empirical measurements and leads to the definition of an effective coarse-grained dynamical skeleton of spatiotemporal brain dynamics.

Human seizures self-terminate across spatial scales via a critical transition

Why seizures spontaneously terminate remains an unanswered fundamental question of epileptology. Here we present evidence that seizures self-terminate via a discontinuous critical transition or bifurcation. We show that human brain electrical activity at various spatial scales exhibits common dynamical signatures of an impending critical transition--slowing, increased correlation, and flickering--in the approach to seizure termination. In contrast, prolonged seizures (status epilepticus) repeatedly approach, but do not cross, the critical transition. To support these results, we implement a computational model that demonstrates that alternative stable attractors, representing the ictal and postictal states, emulate the observed dynamics. These results suggest that self-terminating seizures end through a common dynamical mechanism. This description constrains the specific biophysical mechanisms underlying seizure termination, suggests a dynamical understanding of status epilepticus, and demonstrates an accessible system for studying critical transitions in nature.

Adaptive estimation strategy for coupled EEG-MEG analysis

Human brain electric activity related to important physiological functions can be pictured from bio electric and/or bio magnetic measurements through inverse problems approach. In this paper an iterative strategy is presented. The strategy is optimized to reconstruct brain activity confined in a sub region of the brain volume by jointly processing both bioelectric and biomagnetic signals. The procedure takes advantage from intermediate evaluations to improve the resolution, while limiting the number of unknowns. The advantages of the proposed approach is a higher robustness against noise and uncertainty and better effectiveness. In the last few decades, important contributions in the neuroscience research came from the use of imaging techniques. In particular, new investigation approaches on the anatomy of the brain, based on structuralimagingtechniques,suchascomputeraidedtomography(CT)andmagneticresonanceimaging (MRI), have been proposed. A major field of research in neuroscience is aimed to understand metabolic processes taking place in the brain; this calls for several imaging techniques such as positron emission tomography (PET), single-photon emission tomography (SPECT) and functional magnetic resonance imaging (fMRI) measuring changes of the blood flow or oxygenation in the brain. Such techniques are abletomeasurebrainactivitywithsatisfactoryspatialresolution;unfortunatelythetimeresolutionisquite poor becauseit is limited within the range 0.1 s-1 s. Therefore further techniquescharacterizedby better time resolution, are looked for in the scientific community gathering contributions from mathematical, physicalandengineeringdisciplines. Thesetechniques(1-6)includeelectroencephalography(EEG)and magnetoencephalography(MEG). Electrical currents in the brain can be identified by processingvoltage patterns measured on the scalp (EEG) and/or by measuring the extremely weak magnetic flux density close to the scalp (MEG). The MEG has been recently introduced thanks to the development of the SQUID (Superconducting Quantum Interference Devices) sensors, able to detect the very low magnetic flux density in the extracranial region, in the order of a few fT. Compared with fMRI techniques, EEG and MEG represent an appealing alternative thanks to the time resolution (in the order of 1 ms), and to the lack of radiations (1). Unfortunately their spatial resolution could be not satisfactory (1). In addition, due to the ill posedness of the model, the uncertainty affecting the knowledge of some of the electrical

No effects of a single 3G UMTS mobile phone exposure on spontaneous EEG activity, ERP correlates, and automatic deviance detection

Potential effects of a 30 min exposure to third generation (3G) Universal Mobile Telecommunications System (UMTS) mobile phone-like electromagnetic fields (EMFs) were investigated on human brain electrical activity in two experiments. In the first experiment, spontaneous electroencephalography (sEEG) was analyzed (n = 17); in the second experiment, auditory event-related potentials (ERPs) and automatic deviance detection processes reflected by mismatch negativity (MMN) were investigated in a passive oddball paradigm (n = 26). Both sEEG and ERP experiments followed a double-blind protocol where subjects were exposed to either genuine or sham irradiation in two separate sessions. In both experiments, electroencephalograms (EEG) were recorded at midline electrode sites before and after exposure while subjects were watching a silent documentary. Spectral power of sEEG data was analyzed in the delta, theta, alpha, and beta frequency bands. In the ERP experiment, subjects were presented with a random series of standard (90%) and frequency-deviant (10%) tones in a passive binaural oddball paradigm. The amplitude and latency of the P50, N100, P200, MMN, and P3a components were analyzed. We found no measurable effects of a 30 min 3G mobile phone irradiation on the EEG spectral power in any frequency band studied. Also, we found no significant effects of EMF irradiation on the amplitude and latency of any of the ERP components. In summary, the present results do not support the notion that a 30 min unilateral 3G EMF exposure interferes with human sEEG activity, auditory evoked potentials or automatic deviance detection indexed by MMN.

3D segmented model of head for modelling electrical activity of brain

Computer simulation and modelling of the human body and its behaviour are very useful tools in situations where it is either too risky to perform an invasive procedure or too costly for in vivo experiments or simply impossible for ethical reasons. In this paper we describe a method to model the electrical behaviour of human brain from segmented MR images. The aim of the work is to use these models to predict the electrical activity of human brain under normal and pathological conditions. The image processing software package MIMICS is used for 3D volume segmentation of MR images. These models have detailed 3D representation of major tissue surfaces within the head, with over 12 different tissues segmented. In addition, computational tools in Matlab were developed for calculating normal vectors on the brain surface and for associating this information to the equivalent electrical dipole sources as an input into the model.

3D segmented model of head for modelling electrical activity of brain

Computer simulation and modelling of the human body and its behaviour are very useful tools in situations where it is either too risky to perform an invasive procedure or too costly for in vivo experiments or simply impossible for ethical reasons. In this paper we describe a method to model the electrical behaviour of human brain from segmented MR images. The aim of the work is to use these models to predict the electrical activity of human brain under normal and pathological conditions. The image processing software package MIMICS is used for 3D volume segmentation of MR images. These models have detailed 3D representation of major tissue surfaces within the head, with over 12 different tissues segmented. In addition, computational tools in Matlab were developed for calculating normal vectors on the brain surface and for associating this information to the equivalent electrical dipole sources as an input into the model.

Особенности электрической активности мозга человека при длительном пребывании в условиях Антарктики

Особенности электрической активности мозга человека при длительном пребывании в условиях Антарктики

Numerical analysis of recurrence plots to detect effect of environmental-strength magnetic fields on human brain electrical activity

Environmental magnetic fields may activate the neuroendocrine stressor system leading to some human diseases. The stressor theory predicts that the fields can trigger changes in brain electrical activity, like known stressors. We exposed subjects to 1 and 5 μT, 60 Hz while recording electroencephalograms (EEGs) from six derivations, and used a novel method based on numerical analysis of recurrence plots computed from the signals to detect brain electrical potentials evoked by onset and/or offset of the field. The EEGs were also analyzed using linear methods (time averaging). Evoked potentials occurred in all 22 subjects (family-wise error rate less than 0.05 for each subject); the average latency was 250 ms, as expected based on earlier studies using stronger magnetic fields. Field-induced changes in brain electrical activity were not found using time averaging. Control procedures and measurements obtained from electrical phantoms reasonably excluded recording artifacts or chance as explanations for the effects. Onset and offset of magnetic fields produced immediate changes in brain electrical activity, suggesting that the fields were detected by sensory transduction, like ordinary somatic stressors.

Acceleration of permeability barrier recovery by exposure of skin to 10-30 kHz sound

Previous reports show that ultrasound can influence human brain electrical activity and systemic hormone levels in various parts of the body, other than the ear, so there may be an unknown ultrasound-responsive system in humans. In the present study, we examined the effects of sound on skin permeability barrier homeostasis. We broke the skin barrier of hairless mice by tape stripping, and then exposed the skin to sound for 1 h to evaluate the effect on barrier recovery rate. Exposure of skin to sound at frequencies of 10, 20 and 30 kHz for 1 h accelerated barrier recovery, and 20 kHz sound induced the fastest recovery. Application of 5 kHz sound had no effect on barrier recovery rate. Significant acceleration was observed even when the sound source was located 3 cm away from the skin surface. The recovery rate depended on the sound pressure. An electron-microscopic study indicated that lamellar body secretion between stratum corneum and stratum granulosum was increased by exposure to sound at 20 kHz. These results suggest that epidermal keratinocytes might be influenced by ultrasound in a manner that results in modulation of epidermal permeability barrier homeostasis.

The Effects of Mobile-Phone Electromagnetic Fields on Brain Electrical Activity: A Critical Analysis of the Literature

We analyzed the reports in which human brain electrical activity was compared between the presence and absence of radio-frequency and low-frequency electromagnetic fields (EMFs) from mobile phones, or between pre- and post-exposure to the EMFs. Of 55 reports, 37 claimed and 18 denied an EMF-induced effect on either the baseline electro encephalogram (EEG), or on cognitive processing of visual or auditory stimuli as reflected in changes in event-related potentials. The positive reports did not adequately consider the family-wise error rate, the presence of spike artifacts in the EEG, or the confounding role of the two different EMFs. The negative reports contained neither positive controls nor power analyses. Almost all reports were based on the incorrect assumption that the brain was in equilibrium with its surroundings. Overall, the doubt regarding the existence of reproducible mobile-phone EMFs on brain activity created by the reports appeared to legitimate the knowledge claims of the mobile-phone industry. However, it funded, partly or wholly, at least 87% of the reports. From an analysis of their cognitive framework, the common use of disclaimers, the absence of information concerning conflicts of interest, and the industry's donations to the principal EMF journal, we inferred that the doubt was manufactured by the industry. The crucial scientific question of the pathophysiology of mobile-phone EMFs as reflected in mea surements of brain electrical activity remains unanswered, and essentially unaddressed.

The Effects of Mobile-Phone Electromagnetic Fields on Brain Electrical Activity: A Critical Analysis of the Literature

We analyzed the reports in which human brain electrical activity was compared between the presence and absence of radio-frequency and low-frequency electromagnetic fields (EMFs) from mobile phones, or between pre- and post-exposure to the EMFs. Of 55 reports, 37 claimed and 18 denied an EMF-induced effect on either the baseline electro encephalogram (EEG), or on cognitive processing of visual or auditory stimuli as reflected in changes in event-related potentials. The positive reports did not adequately consider the family-wise error rate, the presence of spike artifacts in the EEG, or the confounding role of the two different EMFs. The negative reports contained neither positive controls nor power analyses. Almost all reports were based on the incorrect assumption that the brain was in equilibrium with its surroundings. Overall, the doubt regarding the existence of reproducible mobile-phone EMFs on brain activity created by the reports appeared to legitimate the knowledge claims of the mobile-phone industry. However, it funded, partly or wholly, at least 87% of the reports. From an analysis of their cognitive framework, the common use of disclaimers, the absence of information concerning conflicts of interest, and the industry's donations to the principal EMF journal, we inferred that the doubt was manufactured by the industry. The crucial scientific question of the pathophysiology of mobile-phone EMFs as reflected in mea surements of brain electrical activity remains unanswered, and essentially unaddressed.