scholarly journals Retrieving the structure of probabilistic sequences of auditory stimuli from EEG data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Noslen Hernández ◽  
Aline Duarte ◽  
Guilherme Ost ◽  
Ricardo Fraiman ◽  
Antonio Galves ◽  
...  
Keyword(s):  
Probabilistic Models ◽  
Brain Activity ◽  
Probabilistic Approach ◽  
Auditory Stimuli ◽  
Labeled Trees ◽  
Eeg Data ◽  
Context Tree ◽  
Probabilistic Sequences ◽  
The Relationship ◽  
The Brain

AbstractUsing a new probabilistic approach we model the relationship between sequences of auditory stimuli generated by stochastic chains and the electroencephalographic (EEG) data acquired while 19 participants were exposed to those stimuli. The structure of the chains generating the stimuli are characterized by rooted and labeled trees whose leaves, henceforth called contexts, represent the sequences of past stimuli governing the choice of the next stimulus. A classical conjecture claims that the brain assigns probabilistic models to samples of stimuli. If this is true, then the context tree generating the sequence of stimuli should be encoded in the brain activity. Using an innovative statistical procedure we show that this context tree can effectively be extracted from the EEG data, thus giving support to the classical conjecture.

Neurodevelopment in the first three years: implications for child development, professional practice and policy

2014 ◽  
Vol 9 (2) ◽  
pp. 154-164 ◽  
Author(s):  
Danya Glaser
Keyword(s):  
Brain Development ◽  
Brain Activity ◽  
Brain Maturation ◽  
Policy And Practice ◽  
Content Type ◽  
Key Issues ◽  
And Function ◽  
The Relationship ◽  
The Brain ◽  
Brain Connections

Purpose – The purpose of this paper is to outline brain structure and development, the relationship between environment and brain development and implications for practice. Design/methodology/approach – The paper is based on a selected review of the literature and clinical experience. Findings – While genetics determine the sequence of brain maturation, the nature of brain development and functioning is determined by the young child's caregiving environment, to which the developing brain constantly adapts. The absence of input during sensitive periods may lead to later reduced functioning. There is an undoubted immediate equivalence between every mind function – emotion, cognition, behaviour and brain activity, although the precise location of this in the brain is only very partially determinable, since brain connections and function are extremely complex. Originality/value – This paper provides an overview of key issues in neurodevelopment relating to the development of young children, and implications for policy and practice.

Mortal Body, Immortal Mind

2012 ◽  
Vol 17 (1) ◽  
pp. 5-26
Author(s):  
Hans Goller
Keyword(s):  
Brain Activity ◽  
The Body ◽  
Activity Data ◽  
Brain Functions ◽  
Body Experiences ◽  
Research Findings ◽  
The Relationship ◽  
Near Death Experiences ◽  
The Brain ◽  
Alternative Theories

Neuroscientists keep telling us that the brain produces consciousness and consciousness does not survive brain death because it ceases when brain activity ceases. Research findings on near-death-experiences during cardiac arrest contradict this widely held conviction. They raise perplexing questions with regard to our current understanding of the relationship between consciousness and brain functions. Reports on veridical perceptions during out-of-body experiences suggest that consciousness may be experienced independently of a functioning brain and that self-consciousness may continue even after the termination of brain activity. Data on studies of near-death-experiences could be an incentive to develop alternative theories of the body-mind relation as seen in contemporary neuroscience.

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 Recording brain activities in unshielded Earth’s field with optically pumped atomic magnetometers

2020 ◽  
Vol 6 (24) ◽  
pp. eaba8792 ◽  
Author(s):  
Rui Zhang ◽  
Wei Xiao ◽  
Yudong Ding ◽  
Yulong Feng ◽  
Xiang Peng ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Medical Diagnosis ◽  
Alpha Rhythm ◽  
Brain Activity ◽  
High Sensitivity ◽  
Noninvasive Method ◽  
Optically Pumped ◽  
Field Noise ◽  
The Relationship ◽  
The Brain

Understanding the relationship between brain activity and specific mental function is important for medical diagnosis of brain symptoms, such as epilepsy. Magnetoencephalography (MEG), which uses an array of high-sensitivity magnetometers to record magnetic field signals generated from neural currents occurring naturally in the brain, is a noninvasive method for locating the brain activities. The MEG is normally performed in a magnetically shielded room. Here, we introduce an unshielded MEG system based on optically pumped atomic magnetometers. We build an atomic magnetic gradiometer, together with feedback methods, to reduce the environment magnetic field noise. We successfully observe the alpha rhythm signals related to closed eyes and clear auditory evoked field signals in unshielded Earth’s field. Combined with improvements in the miniaturization of the atomic magnetometer, our method is promising to realize a practical wearable and movable unshielded MEG system and bring new insights into medical diagnosis of brain symptoms.

scholarly journals No Detectable Electroencephalographic Activity After Clinical Declaration of Death Among Tibetan Buddhist Meditators in Apparent Tukdam, a Putative Postmortem Meditation State

2021 ◽  
Vol 11 ◽  
Author(s):  
Dylan T. Lott ◽  
Tenzin Yeshi ◽  
N. Norchung ◽  
Sonam Dolma ◽  
Nyima Tsering ◽  
...  
Keyword(s):  
Brain Activity ◽  
Auditory Brainstem ◽  
Tibetan Buddhist ◽  
University Of Wisconsin ◽  
Brainstem Response ◽  
The University ◽  
Electroencephalographic Activity ◽  
The Relationship ◽  
The Brain ◽  
Early Postmortem

Recent EEG studies on the early postmortem interval that suggest the persistence of electrophysiological coherence and connectivity in the brain of animals and humans reinforce the need for further investigation of the relationship between the brain’s activity and the dying process. Neuroscience is now in a position to empirically evaluate the extended process of dying and, more specifically, to investigate the possibility of brain activity following the cessation of cardiac and respiratory function. Under the direction of the Center for Healthy Minds at the University of Wisconsin-Madison, research was conducted in India on a postmortem meditative state cultivated by some Tibetan Buddhist practitioners in which decomposition is putatively delayed. For all healthy baseline (HB) and postmortem (PM) subjects presented here, we collected resting state electroencephalographic data, mismatch negativity (MMN), and auditory brainstem response (ABR). In this study, we present HB data to demonstrate the feasibility of a sparse electrode EEG configuration to capture well-defined ERP waveforms from living subjects under very challenging field conditions. While living subjects displayed well-defined MMN and ABR responses, no recognizable EEG waveforms were discernable in any of the tukdam cases.

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.

Induced Theta Activity as a Biomarker for a Morbid Effect of Alcoholism on the Brain in Long-Term Abstinent Alcoholics

2013 ◽  
Vol 27 (2) ◽  
pp. 76-83 ◽  
Author(s):  
Casey S. Gilmore ◽  
George Fein
Keyword(s):  
Brain Function ◽  
Target Stimulus ◽  
Brain Activity ◽  
Theta Activity ◽  
Theta Oscillations ◽  
Time Frequency ◽  
Related Information ◽  
The Relationship ◽  
The Brain

Event-related, target stimulus-phase-locked (evoked) brain activity in both the time and time-frequency (TF) domains (the P3b ERP; evoked theta oscillations) has been shown to be reduced in alcoholics. Recently, studies have suggested that there is alcohol-related information in the non-stimulus-phase-locked (induced) theta TF activity. We applied TF analysis to target stimulus event-related EEG recorded during an oddball task from 41 long-term abstinent alcoholics (LTAA) and 74 nonalcoholic controls (NAC) to investigate the relationship between P3b, evoked theta, and induced theta activity. Results showed that an event-related synchronization (ERS) of induced theta (1) was larger in LTAA compared to NAC, and (2) was sensitive to differences between LTAA and NAC groups that was independent of the differences accounted for by P3b amplitude or evoked theta. These findings suggest that increased induced theta ERS may likely be a biomarker for a morbid effect of alcohol abuse on brain function.

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.

MONOTONOUS TASKS AND ALCOHOL CONSUMPTION EFFECTS ON THE BRAIN BY EEG ANALYSIS USING NEURAL NETWORKS

2012 ◽  
Vol 11 (03) ◽  
pp. 1250015 ◽  
Author(s):  
STEPHEN KARUNGARU ◽  
TOSHIHIRO YOSHIDA ◽  
TORU SEO ◽  
MINORU FUKUMI ◽  
KENJI TERADA
Keyword(s):  
Neural Networks ◽  
Mental Stress ◽  
Brain Activity ◽  
Principal Component ◽  
Eeg Signals ◽  
Linear Discriminant ◽  
The Relationship ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Eeg Signal Analysis

An analysis of the Electroencephalogram (EEG) signals while performing a monotonous task and drinking alcohol using principal component analysis (PCA), linear discriminant analysis (LDA) for feature extraction and Neural Networks (NNs) for classification is proposed. The EEG is captured while performing a monotonous task that can adversely affect the brain and possibly cause stress. Moreover, we investigate the effects of alcohol on the brain by capturing the data continuously after consumption of equal amounts of alcohol. We hope that our work will shed more light on the relationship between such actions and EEG, and investigate if there is any relation between the tasks and mental stress. EEG signals offers a rare look at brain activity, while, monotonous activities are well known to cause irritation which may contribute to mental stress. We apply PCA and LDA to characterize the change in each component, extract it and discriminate using a NN. After experiments, it was found that PCA and LDA are effective analysis methods in EEG signal analysis.

COMPLEXITY-BASED ANALYSIS OF BRAINS’ SYNCHRONIZATION IN HUMAN–HUMAN INTERACTION

Fractals ◽  
2020 ◽  
Vol 28 (07) ◽  
pp. 2050102 ◽  
Author(s):  
MOHAMED RASMI ASHFAQ AHAMED ◽  
MOHAMMAD HOSSEIN BABINI ◽  
NAJMEH PAKNIYAT ◽  
HAMIDREZA NAMAZI
Keyword(s):  
Fractal Analysis ◽  
Brain Activity ◽  
Human Interaction ◽  
Physiological Signals ◽  
Eeg Signals ◽  
Human Voice ◽  
The Mean ◽  
First Time ◽  
The Relationship ◽  
The Brain

Talking is the most common type of human interaction that people have in their daily life. Besides all conducted studies on the analysis of human behavior in different conditions, no study has been reported yet that analyzed how the brain activity of two persons is related during their conversation. In this research, for the first time, we investigate the relationship between brain activities of people while communicating, considering human voice as the mean of this connection. For this purpose, we employ fractal analysis in order to investigate how the complexity of electroencephalography (EEG) signals for two persons are related. The results showed that the variations of complexity of EEG signals for two persons are correlated while communicating. Statistical analysis also supported the result of analysis. Therefore, it can be stated that the brain activities of two persons are correlated during communication. Fractal analysis can be employed to analyze the correlation between other physiological signals of people while communicating.

Sign in / Sign up

Export Citation Format

Share Document