scholarly journals High-frequency electrical activity of the brain in patients with hemispheric ischemic stroke in combination with cognitive functions

2021 ◽  
Vol 25 (6) ◽  
pp. 12-18
Author(s):  
L. B. Novikova ◽  
K. M. Sharapova ◽  
O. E. Dmitrieva
Keyword(s):  
Ischemic Stroke ◽  
Mathematical Analysis ◽  
Depressive Disorders ◽  
Brain Activity ◽  
Power Spectra ◽  
Peak Frequency ◽  
Gamma Rhythm ◽  
Eeg Data ◽  
Background Eeg ◽  
The Brain

Abstract. The mathematical analysis of electroencephalography (EEG) provides information about the functional state of the brain, expands the understanding of the mechanisms of interaction between different areas of the brain, increases the possibilities of diagnostics and allows to put forward new tasks in the field of studying brain activity. Aim. To assess changes in the gamma-rhythm in patients with hemispheric ischemic stroke in the most acute and acute periods in comparison with cognitive and anxiety-depressive disorders. Material and methods. The study included 32 patients with hemispheric ischemic stroke. All patients underwent complex clinical, neurological, instrumental and laboratory studies. The study and recording of the EEG was carried out on the 1st and 21st days of the disease, lasting 20 minutes. The method of mathematical analysis was used to estimate the power spectra and the peak frequency of the gamma — rhythm of the background EEG. Results. As a result of the study, it was found that cognitive and anxiety-depressive disorders are detected already in the most acute and acute periods of ischemic stroke. In the mathematical analysis of the EEG statistically significant correlations between the gamma — rhythm index and cognitive, anxiety-depressive disorders in the frontal, central temporal areas are noted. Conclusion. The complex of examination of patients should include, in addition to clinical and neuropsychological research, mathematical analysis of EEG data.

THE POSSIBILITIES OF ELECTROENCEPHALOGRAPHY IN ASSESSING THE COLLATERAL BLOOD CIRCULATION OF THE HUMAN BRAIN

2021 ◽  
Author(s):  
Olga B. Sazonova ◽  
◽  
Anna A. Ogurtsova ◽  
Elena Troshina ◽  
Eugene Macherov ◽  
...  
Keyword(s):  
Mathematical Analysis ◽  
Visual Analysis ◽  
Brain Activity ◽  
Power Spectra ◽  
Cerebral Aneurysms ◽  
Close Correlation ◽  
Pathological Process ◽  
Mathematical Methods ◽  
Anal Ysis ◽  
Ischemic Complications

The purpose of this study was to show the role and capabilities of EEG in the assessment of collateral brain circulation (CBM). The paper presents the results of a retrospective EEG anal-ysis of 210 patients with large and giant cerebral aneurysms of various localization, who, in the order of neurosurgical treatment, had to turn off the vessel carrying the aneurysm. We used a test with compression of the carotid artery in the neck, leading to restriction of blood flow through the main vessels of the head. For EEG analysis, visual and mathematical methods were used with the calculation of power spectra and EEG coherence spectra, as well as the “dist” parameter characterizing deviations of parameters from the averaged norm data and with dif-ferent forms of CBM compensation. The use of methods of mathematical analysis significantly expands the diagnostic capabilities of EEG in assessing CBM, allowing you to identify changes in bioelectrical brain activity, hidden during routine visual analysis and express them in a more informative, quantitative form. Based on the data obtained, during visual analysis of the EEG, we identified three forms of CBM: compensated, subcompensated, and decompensated. The work shows that there is a close correlation between the parameters of the average levels of frequency, amplitude and coherent EEG indicators in various forms of compensation for collateral circulation of the brain, The most informative from the used parameters of the EEG mathematical analysis, in our opinion, is the criterion of deviations "dist". It does not depend on the initial level of EEG parameters and may be used in normal conditions, in the presence of a pathological process and in the presence of compression of the SA on the neck. This method is used to prevent ischemic complications during the operation with the planned shutdown of the vessels.

Classification Algorithms for EEG-Based Brain-Computer Interface

2019 ◽  
pp. 52-73
Author(s):  
Sravanth Kumar Ramakuri ◽  
Chinmay Chakraboirty ◽  
Anudeep Peddi ◽  
Bharat Gupta
Keyword(s):  
Brain Activity ◽  
Biomedical Signal Processing ◽  
Computer Interface ◽  
Eeg Signals ◽  
Large Sets ◽  
Eeg Data ◽  
Important Measurement ◽  
The Brain

In recent years, a vast research is concentrated towards the development of electroencephalography (EEG)-based human-computer interface in order to enhance the quality of life for medical as well as nonmedical applications. The EEG is an important measurement of brain activity and has great potential in helping in the diagnosis and treatment of mental and brain neuro-degenerative diseases and abnormalities. In this chapter, the authors discuss the classification of EEG signals as a key issue in biomedical research for identification and evaluation of the brain activity. Identification of various types of EEG signals is a complicated problem, requiring the analysis of large sets of EEG data. Representative features from a large dataset play an important role in classifying EEG signals in the field of biomedical signal processing. So, to reduce the above problem, this research uses three methods to classify through feature extraction and classification schemes.

scholarly journals Synchronization in the connectome: Metastable oscillatory modes emerge from interactions in the brain spacetime network

2022 ◽  
Author(s):  
Joana Cabral ◽  
Francesca Castaldo ◽  
Jakub Vohryzek ◽  
Vladimir Litvak ◽  
Christian Bick ◽  
...  
Keyword(s):  
Coupled Oscillators ◽  
Brain Activity ◽  
Power Spectra ◽  
Brain Areas ◽  
Neural Mass ◽  
Spatially Distributed ◽  
Weakly Stable ◽  
Human Brains ◽  
The Brain

A rich repertoire of oscillatory signals is detected from human brains with electro- and magnetoencephalography (EEG/MEG). However, the principles underwriting coherent oscillations and their link with neural activity remain unclear. Here, we hypothesise that the emergence of transient brain rhythms is a signature of weakly stable synchronization between spatially distributed brain areas, occurring at network-specific collective frequencies due to non-negligible conduction times. We test this hypothesis using a phenomenological network model to simulate interactions between neural mass potentials (resonating at 40Hz) in the structural connectome. Crucially, we identify a critical regime where metastable oscillatory modes emerge spontaneously in the delta (0.5-4Hz), theta (4-8Hz), alpha (8-13Hz) and beta (13-30Hz) frequency bands from weak synchronization of subsystems, closely approximating the MEG power spectra from 89 healthy individuals. Grounded in the physics of delay-coupled oscillators, these numerical analyses demonstrate the role of the spatiotemporal connectome in structuring brain activity in the frequency domain.

scholarly journals EEG Characterization of the Alzheimer’s Disease Continuum by Means of Multiscale Entropies

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 544 ◽  
Author(s):  
Aarón Maturana-Candelas ◽  
Carlos Gómez ◽  
Jesús Poza ◽  
Nadia Pinto ◽  
Roberto Hornero
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disorder ◽  
Brain Activity ◽  
P Values ◽  
Eeg Data ◽  
High Prevalence ◽  
Average Slope ◽  
The Brain

Alzheimer’s disease (AD) is a neurodegenerative disorder with high prevalence, known for its highly disabling symptoms. The aim of this study was to characterize the alterations in the irregularity and the complexity of the brain activity along the AD continuum. Both irregularity and complexity can be studied applying entropy-based measures throughout multiple temporal scales. In this regard, multiscale sample entropy (MSE) and refined multiscale spectral entropy (rMSSE) were calculated from electroencephalographic (EEG) data. Five minutes of resting-state EEG activity were recorded from 51 healthy controls, 51 mild cognitive impaired (MCI) subjects, 51 mild AD patients (ADMIL), 50 moderate AD patients (ADMOD), and 50 severe AD patients (ADSEV). Our results show statistically significant differences (p-values < 0.05, FDR-corrected Kruskal–Wallis test) between the five groups at each temporal scale. Additionally, average slope values and areas under MSE and rMSSE curves revealed significant changes in complexity mainly for controls vs. MCI, MCI vs. ADMIL and ADMOD vs. ADSEV comparisons (p-values < 0.05, FDR-corrected Mann–Whitney U-test). These findings indicate that MSE and rMSSE reflect the neuronal disturbances associated with the development of dementia, and may contribute to the development of new tools to track the AD progression.

scholarly journals Relationship between Spatiotemporal Dynamics of the Brain at Rest and Self-Reported Spontaneous Thoughts: An EEG Microstate Approach

2021 ◽  
Vol 11 (11) ◽  
pp. 1216
Author(s):  
Povilas Tarailis ◽  
Dovilė Šimkutė ◽  
Thomas Koenig ◽  
Inga Griškova-Bulanova
Keyword(s):  
Resting State ◽  
Brain Activity ◽  
Subjective Experiences ◽  
Somatic Awareness ◽  
Substantial Evidence ◽  
Temporal Properties ◽  
Eeg Data ◽  
Spontaneous Thought ◽  
Intrinsic Brain Activity ◽  
The Brain

Rationale: The resting-state paradigm is frequently applied in electroencephalography (EEG) research; however, it is associated with the inability to control participants’ thoughts. To quantify subjects’ subjective experiences at rest, the Amsterdam Resting-State Questionnaire (ARSQ) was introduced covering ten dimensions of mind wandering. We aimed to estimate associations between subjective experiences and resting-state microstates of EEG. Methods: 5 min resting-state EEG data of 197 subjects was used to evaluate temporal properties of seven microstate classes. Bayesian correlation approach was implemented to assess associations between ARSQ domains assessed after resting and parameters of microstates. Results: Several associations between Comfort, Self and Somatic Awareness domains and temporal properties of neuroelectric microstates were revealed. The positive correlation between Comfort and duration of microstates E showed the strongest evidence (BF10 > 10); remaining correlations showed substantial evidence (10 > BF10 > 3). Conclusion: Our study indicates the relevance of assessments of spontaneous thought occurring during the resting-state for the understanding of the intrinsic brain activity reflected in microstates.

scholarly journals The impact of mental fatigue on brain activity: A comparative study between resting state and task state using EEG

2019 ◽  
Author(s):  
Gang Li ◽  
Youdong Luo ◽  
Weidong Jiao ◽  
Yonghua Jiang ◽  
Zhao Gao ◽  
...  
Keyword(s):  
Resting State ◽  
Mental Arithmetic ◽  
Brain Activity ◽  
Mental Fatigue ◽  
Relative Power ◽  
Arithmetic Task ◽  
Eeg Data ◽  
Mental Arithmetic Task ◽  
The Brain ◽  
And Task

Abstract Background: Mental fatigue is usually caused by long-term cognitive activities, mainly manifested as drowsiness, difficulty in concentrating, decreased alertness, disordered thinking, slow reaction, lethargy, reduced work efficiency, error-prone and so on. Mental fatigue has become a widespread sub-health condition, and has a serious impact on the cognitive function of the brain. However, seldom researches explore the differences of mental fatigue on electrophysiological activity between resting state and task state. In the present study, 20 healthy male individuals were recruited to do a consecutive mental arithmetic task to induce mental fatigue, and scalp electroencephalogram (EEG) data were collected before and after the task. The power and relative power of five EEG rhythms both in resting state and task state were analyzed statistically. Results: The results of brain topographies and statistical analysis indicated that mental arithmetic task can successfully induce mental fatigue in the enrolled subjects. The relative power index was more sensitive than the power index in response to mental fatigue, and the relative power for assessing mental fatigue was better in resting state than in task state. Furthermore, we found that it is of great physiological significance to divide alpha frequency band into alpha1 band and alpha2 band in fatigue related studies, and at the same time improve the statistical differences of sub-bands. Conclusions: Our current results suggested that the brain activity in mental fatigue state has great differences between resting state and task state, and it is imperative to select the appropriate state in EEG data acquisition and divide alpha band into alpha1 and alpha2 bands in mental fatigue related researches.

scholarly journals Selfhood triumvirate: from phenomenology to brain activity and back again

2020 ◽  
Author(s):  
Andrew Fingelkurts ◽  
Alexander Fingelkurts ◽  
Tarja Kallio-Tamminen
Keyword(s):  
Brain Activity ◽  
Sense Of Self ◽  
Three Dimensional ◽  
Integrative Model ◽  
Independent Manner ◽  
Functional Integrity ◽  
Causative Relationship ◽  
Eeg Data ◽  
The Brain

Recently, a three-dimensional construct model for complex experiential Selfhood has been proposed (Fingelkurts et al., 2016b,c). According to this model, three specific subnets (or modules) of the brain self-referential network (SRN) are responsible for the manifestation of three aspects/features of the subjective sense of Selfhood. Follow up multiple studies established a tight relation between alterations in the functional integrity of the triad of SRN modules and related to them three aspects/features of the sense of self; however, the causality of this relation is yet to be shown. In this article we approached the question of causality by exploring functional integrity within the three SRN modules that are thought to underlie the three phenomenal components of Selfhood while these components were manipulated mentally by experienced meditators in a controlled and independent manner. Participants were requested, in a block-randomised manner, to mentally induce states representing either increased (up-regulation) or decreased (down-regulation) sense of (a) witnessing agency (“Self”), or (b) body representational-emotional agency (“Me”), or (c) reflective/narrative agency (“I”), while their brain activity was recorded by an electroencephalogram (EEG). This EEG-data was complemented by first-person phenomenological reports and standardised questionnaires which focused on subjective contents of three aspects of Selfhood. The results of the study strengthen the case for a direct causative relationship between three phenomenological aspects of Selfhood and related to them three modules of the brain SRN. Furthermore, the putative integrative model of the dynamic interrelations among three modules of the SRN has been proposed.

scholarly journals Exploring electroencephalography with a model inspired by quantum mechanics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nicholas J. M. Popiel ◽  
Colin Metrow ◽  
Geoffrey Laforge ◽  
Adrian M. Owen ◽  
Bobby Stojanoski ◽  
...  
Keyword(s):  
Quantum Mechanics ◽  
Brain Activity ◽  
Brain State ◽  
The Novel ◽  
Heisenberg Uncertainty Principle ◽  
Eeg Data ◽  
Organizational Principle ◽  
Heisenberg Uncertainty ◽  
Task Conditions ◽  
The Brain

AbstractAn outstanding issue in cognitive neuroscience concerns how the brain is organized across different conditions. For instance, during the resting-state condition, the brain can be clustered into reliable and reproducible networks (e.g., sensory, default, executive networks). Interestingly, the same networks emerge during active conditions in response to various tasks. If similar patterns of neural activity have been found across diverse conditions, and therefore, different underlying processes and experiences of the environment, is the brain organized by a fundamental organizational principle? To test this, we applied mathematical formalisms borrowed from quantum mechanisms to model electroencephalogram (EEG) data. We uncovered a tendency for EEG signals to be localized in anterior regions of the brain during “rest”, and more uniformly distributed while engaged in a task (i.e., watching a movie). Moreover, we found analogous values to the Heisenberg uncertainty principle, suggesting a common underlying architecture of human brain activity in resting and task conditions. This underlying architecture manifests itself in the novel constant KBrain, which is extracted from the brain state with the least uncertainty. We would like to state that we are using the mathematics of quantum mechanics, but not claiming that the brain behaves as a quantum object.

scholarly journals Application of functional near-infrared spectroscopy in the healthcare industry: A review

2019 ◽  
Vol 12 (06) ◽  
pp. 1930012 ◽  
Author(s):  
Keum-Shik Hong ◽  
M. Atif Yaqub
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Depressive Disorders ◽  
Near Infrared ◽  
Brain Activity ◽  
Autism Spectrum ◽  
Future Research ◽  
Functional Near Infrared Spectroscopy ◽  
Brain Hemodynamics ◽  
The Brain

Functional near-infrared spectroscopy (fNIRS), a growing neuroimaging modality, has been utilized over the past few decades to understand the neuronal behavior in the brain. The technique has been used to assess the brain hemodynamics of impaired cohorts as well as able-bodied. Neuroimaging is a critical technique for patients with impaired cognitive or motor behaviors. The portable nature of the fNIRS system is suitable for frequent monitoring of the patients who exhibit impaired brain activity. This study comprehensively reviews brain-impaired patients: The studies involving patient populations and the diseases discussed in more than 10 works are included. Eleven diseases examined in this paper include autism spectrum disorder, attention-deficit hyperactivity disorder, epilepsy, depressive disorders, anxiety and panic disorder, schizophrenia, mild cognitive impairment, Alzheimer’s disease, Parkinson’s disease, stroke, and traumatic brain injury. For each disease, the tasks used for examination, fNIRS variables, and significant findings on the impairment are discussed. The channel configurations and the regions of interest are also outlined. Detecting the occurrence of symptoms at an earlier stage is vital for better rehabilitation and faster recovery. This paper illustrates the usability of fNIRS for early detection of impairment and the usefulness in monitoring the rehabilitation process. Finally, the limitations of the current fNIRS systems (i.e., nonexistence of a standard method and the lack of well-established features for classification) and future research directions are discussed. The authors hope that the findings in this paper would lead to advanced breakthrough discoveries in the fNIRS field in the future.

scholarly journals Localizing Brain Activity from Multiple Distinct Sources via EEG

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
George Dassios ◽  
Michael Doschoris ◽  
Konstantia Satrazemi
Keyword(s):  
Inverse Problem ◽  
Surface Potential ◽  
Brain Activity ◽  
Sufficient Conditions ◽  
Measured Data ◽  
Single Dipole ◽  
Eeg Data ◽  
Necessary And Sufficient ◽  
Spherical Conductor ◽  
The Brain

An important question arousing in the framework of electroencephalography (EEG) is the possibility to recognize, by means of a recorded surface potential, the number of activated areas in the brain. In the present paper, employing a homogeneous spherical conductor serving as an approximation of the brain, we provide a criterion which determines whether the measured surface potential is evoked by a single or multiple localized neuronal excitations. We show that the uniqueness of the inverse problem for a single dipole is closely connected with attaining certain relations connecting the measured data. Further, we present the necessary and sufficient conditions which decide whether the collected data originates from a single dipole or from numerous dipoles. In the case where the EEG data arouses from multiple parallel dipoles, an isolation of the source is, in general, not possible.

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