scholarly journals Personalized Theta and Beta Binaural Beats for Brain Entrainment: An Electroencephalographic Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
César E. Corona-González ◽  
Luz María Alonso-Valerdi ◽  
David I. Ibarra-Zarate
Keyword(s):  
Resting State ◽  
Auditory Perception ◽  
Mental Condition ◽  
Eeg Signals ◽  
Binaural Beats ◽  
Power Spectral ◽  
Power Change ◽  
Pure Tones ◽  
The Right ◽  
The Brain

Binaural beats (BB) consist of two slightly distinct auditory frequencies (one in each ear), which are differentiated with clinical electroencephalographic (EEG) bandwidths, namely, delta, theta, alpha, beta, or gamma. This auditory stimulation has been widely used to module brain rhythms and thus inducing the mental condition associated with the EEG bandwidth in use. The aim of this research was to investigate whether personalized BB (specifically those within theta and beta EEG bands) improve brain entrainment. Personalized BB consisted of pure tones with a carrier tone of 500 Hz in the left ear together with an adjustable frequency in the right ear that was defined for theta BB (since fc for theta EEG band was 4.60 Hz ± 0.70 SD) and beta BB (since fc for beta EEG band was 18.42 Hz ± 2.82 SD). The adjustable frequencies were estimated for each participant in accordance with their heart rate by applying the Brain-Body Coupling Theorem postulated by Klimesch. To achieve this aim, 20 healthy volunteers were stimulated with their personalized theta and beta BB for 20 min and their EEG signals were collected with 22 channels. EEG analysis was based on the comparison of power spectral density among three mental conditions: (1) theta BB stimulation, (2) beta BB stimulation, and (3) resting state. Results showed larger absolute power differences for both BB stimulation sessions than resting state on bilateral temporal and parietal regions. This power change seems to be related to auditory perception and sound location. However, no significant differences were found between theta and beta BB sessions when it was expected to achieve different brain entrainments, since theta and beta BB induce relaxation and readiness, respectively. In addition, relative power analysis (theta BB/resting state) revealed alpha band desynchronization in the parieto-occipital region when volunteers listened to theta BB, suggesting that participants felt uncomfortable. In conclusion, neural resynchronization was met with both personalized theta and beta BB, but no different mental conditions seemed to be achieved.

scholarly journals Mindfulness Meditation Is Related to Long-Lasting Changes in Hippocampal Functional Topology during Resting State: A Magnetoencephalography Study

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Anna Lardone ◽  
Marianna Liparoti ◽  
Pierpaolo Sorrentino ◽  
Rosaria Rucco ◽  
Francesca Jacini ◽  
...  
Keyword(s):  
Resting State ◽  
Mindfulness Meditation ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Brain Network ◽  
Long Term Effects ◽  
Age Related ◽  
Functional Topology ◽  
The Right ◽  
The Brain

It has been suggested that the practice of meditation is associated to neuroplasticity phenomena, reducing age-related brain degeneration and improving cognitive functions. Neuroimaging studies have shown that the brain connectivity changes in meditators. In the present work, we aim to describe the possible long-term effects of meditation on the brain networks. To this aim, we used magnetoencephalography to study functional resting-state brain networks in Vipassana meditators. We observed topological modifications in the brain network in meditators compared to controls. More specifically, in the theta band, the meditators showed statistically significant (p corrected = 0.009) higher degree (a centrality index that represents the number of connections incident upon a given node) in the right hippocampus as compared to controls. Taking into account the role of the hippocampus in memory processes, and in the pathophysiology of Alzheimer’s disease, meditation might have a potential role in a panel of preventive strategies.

scholarly journals Using Nonlinear Dynamics and Multivariate Statistics to Analyze EEG Signals of Insomniacs with the Intervention of Superficial Acupuncture

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Shi-Yi Qi ◽  
Dong Lin ◽  
Li-Li Lin ◽  
Xiao-Zhen Huang ◽  
Shen Lin ◽  
...  
Keyword(s):  
Nonlinear Dynamics ◽  
Multivariate Statistics ◽  
Resting State ◽  
Repeated Measures ◽  
Occipital Lobe ◽  
Frontal Lobes ◽  
Brain Regions ◽  
Primary Insomnia ◽  
Eeg Signals ◽  
The Right

Objective. As a noninvasive and nonpharmacological therapeutic approach, superficial acupuncture (SA) is a special method of acupuncture. In this study, using nonlinear dynamics and multivariate statistics, we studied the electroencephalography (EEG) of primary insomnia under SA intervention to investigate how brain regions change. Method. This study included 30 adults with primary insomnia. They underwent superficial acupuncture at the Shangen acupoint. The EEG signals were collected for 10 minutes at each state, including the resting state, the intervention state, and the postintervention state. The data were conducted using nonlinear dynamics (including approximate entropy (ApEn) and correlation dimension (CD)) and multivariate statistics. Result. The repeated-measures ANOVA results showed that both ApEn and CD values were not significantly different at the three states p > 0.05 . The paired t-test results showed that the ApEn values of electrodes O2 (the right occipital lobe) at the postintervention state have decreased, compared with the resting state p < 0.05 , and no difference was detected in CD p > 0.05 . The cluster analysis results of ApEn showed that patients’ EEG has changed from the right prefrontal lobe (electrode Fp2) to the right posterior temporal lobe (electrode T6) and finally to the right occipital lobe (electrode O2), before, during, and after the SA intervention. In addition, the factor analysis results of CD revealed that patients’ EEG of all brain regions except for the occipital lobes has changed to the frontal lobes and anterior temporal and frontal lobes from pre- to postintervention. Conclusion. SA activated the corresponding brain regions and reduced the complexity of the brain involved. It is feasible to use nonlinear dynamics analysis and multivariate statistics to examine the effects of SA on the human brain.

scholarly journals Investigation of EEG signals generated by motor imagery for application in BCIs

2018 ◽  
Author(s):  
Caique de Medeiros Mendes ◽  
Gabriela Castellano ◽  
Carlos Alberto Stefano Filho
Keyword(s):  
Motor Imagery ◽  
Low Complexity ◽  
Eeg Signals ◽  
Brain Response ◽  
K Nearest Neighbors ◽  
Computer Interfaces ◽  
Power Spectral ◽  
Motor Movements ◽  
Accuracy Rates ◽  
The Brain

Motor imagery (MI) is a commonly used strategy in brain-computer interfaces (BCIs) to modify neuronal activity, in which the user, by imagining motor movements, generates signals that can be recorded and interpreted to control a device. In this study, we sought to investigate how the brain response of users during MI happens, by analyzing a database of EEG signals in which healthy subjects were asked to imagine the movement of their right and left hands. Our goal was to recognize patterns associated with this task, through a spectral evaluation of different segments of the signal. Therefore, we estimated the power spectral density (PSD) for each evaluated segment and then used it for classification, via k-nearest neighbors (k-NN). We found that the accuracy rates obtained with k-NN classification were very similar to random, suggesting, mainly, high inter-subjects variability and choice of a low complexity classifier.

REDUNDANCY IN FUNCTIONAL BRAIN CONNECTIVITY FROM EEG RECORDINGS

2012 ◽  
Vol 22 (07) ◽  
pp. 1250158 ◽  
Author(s):  
FABRIZIO DE VICO FALLANI ◽  
JLENIA TOPPI ◽  
CLAUDIA DI LANZO ◽  
GIOVANNI VECCHIATO ◽  
LAURA ASTOLFI ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Random Networks ◽  
Eeg Signals ◽  
Statistical Comparison ◽  
Natural Tendency ◽  
Critical Resource ◽  
Path Lengths ◽  
Eeg Recordings ◽  
The Brain

The concept of redundancy is a critical resource of the brain enhancing the resilience to neural damages and dysfunctions. In the present work, we propose a graph-based methodology to investigate the connectivity redundancy in brain networks. By taking into account all the possible paths between pairs of nodes, we considered three complementary indexes, characterizing the network redundancy (i) at the global level, i.e. the scalar redundancy (ii) across different path lengths, i.e. the vectorial redundancy (iii) between node pairs, i.e. the matricial redundancy. We used this procedure to investigate the functional connectivity estimated from a dataset of high-density EEG signals in a group of healthy subjects during a no-task resting state. The statistical comparison with a benchmark dataset of random networks, having the same number of nodes and links of the EEG nets, revealed a significant (p < 0.05) difference for all the three indexes. In particular, the redundancy in the EEG networks, for each frequency band, appears radically higher than random graphs, thus revealing a natural tendency of the brain to present multiple parallel interactions between different specialized areas. Notably, the matricial redundancy showed a high (p < 0.05) redundancy between the scalp sensors over the parieto-occipital areas in the Alpha range of EEG oscillations (7.5–12.5 Hz), which is known to be the most responsive channel during resting state conditions.

scholarly journals A Cognitive Workload Identification using EEG Power Spectrum

2019 ◽  
Vol 8 (4) ◽  
pp. 8517-8524
Keyword(s):  
Power Spectrum ◽  
Resting State ◽  
Power Distribution ◽  
Low Cost ◽  
Power Spectral Analysis ◽  
Cognitive Workload ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Statistical Parameters ◽  
Power Spectral

Now a days, Electroencephalography (EEG) is popular to monitor human’s cognitive workload. EEG signals are delicate to the variation in cognitive load in various fields including observing cognitive workload for the intricate environment of military chores. Earlier to acquire the EEG signals high-cost EEG systems were used which bounds their use but now a day’s low-cost headsets are available to capture EEG which makes it a promising set-up to measure cognitive workload. EEGs are initially preprocessed to reflect the artifacts present in it. After preprocessing, signals are ready for further processing. The power spectral density corresponds to the power distribution of EEG signal in the frequency domain which is used to assess the changes in the pattern of the brain. This paper discusses the present progress of research in cognitive workload identification and identifies the techniques associated with the cognitive workload. This proposed research gives the analysis of EEG signal power spectrum density (PSD) during resting state and cognitive workload activities of a human. With power spectral analysis of the EEG signal, seven statistical parameters have been calculated (minimum, maximum, mean, median, mode, standard deviation and range) have been calculated Analysis showed that the in cognitive workload, PSD has significantly changed if compared to the resting state

scholarly journals EEG power spectral density in locked-in and completely locked-in state patients: a longitudinal study

2020 ◽  
Author(s):  
Arianna Secco ◽  
Alessandro Tonin ◽  
Aygul Rana ◽  
Andres Jaramillo-Gonzalez ◽  
Majid Khalili-Ardali ◽  
...  
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Resting State ◽  
Relevant Information ◽  
Eeg Spectrum ◽  
Power Spectral ◽  
Significant Difference ◽  
Communication Ability ◽  
The Brain ◽  
Lateral Sclerosis

Abstract Persons with their eye closed and without any means of communication is said to be in a completely locked-in state (CLIS) while when they could still open their eyes actively or passively and have some means of communication are said to be in locked-in state (LIS). Two patients in CLIS without any means of communication, and one patient in the transition from LIS to CLIS with means of communication, who have Amyotrophic Lateral Sclerosis were followed at a regular interval for more than 1 year. During each visit, resting-state EEG was recorded before the brain–computer interface (BCI) based communication sessions. The resting-state EEG of the patients was analyzed to elucidate the evolution of their EEG spectrum over time with the disease’s progression to provide future BCI-research with the relevant information to classify changes in EEG evolution. Comparison of power spectral density (PSD) of these patients revealed a significant difference in the PSD’s of patients in CLIS without any means of communication and the patient in the transition from LIS to CLIS with means of communication. The EEG of patients without any means of communication is devoid of alpha, beta, and higher frequencies than the patient in transition who still had means of communication. The results show that the change in the EEG frequency spectrum may serve as an indicator of the communication ability of such patients.

scholarly journals Bioelectric activity of the brain in 3-4 years old children in eyes-open resting state

2021 ◽  
Vol 8 (4) ◽  
pp. 47-56
Author(s):  
V. B. Voitenkov ◽  
A. B. A. B. Palchick ◽  
N. A. Savelieva ◽  
E. P. Bogdanova
Keyword(s):  
Resting State ◽  
Alpha Rhythm ◽  
Average Power ◽  
Pathological State ◽  
Temporal Area ◽  
Main Technique ◽  
Eyes Open ◽  
The Right ◽  
The Brain ◽  
Active Wakefulness

Background. Electroencephalography is the main technique for assessing the functional state of the brain. Indications for EEG are diagnosis of paroxysmal states, prediction of the outcome of a pathological state, evaluation of bioelectrical activity if brain death is suspected. Up to 90 % of the native EEG in calm wakefulness in healthy individuals is occupied by “alpha activity”. In children in active wakefulness, the EEG pattern depends to a great extent on their age.Objective. The aim of the work was to assess EEG parameters in children aged 3–4 years in eyes-open resting state. Design and methods. 31 healthy participants aged 3–4 years were enrolled. EEG was registered for 30 minutes in a state of passive wakefulness in the supine position with open eyes. Average values of the power of the spectra for the alpha-rhythm, delta-rhythm and theta-rhythm in the frontal and temporal leads, as well as the ratio of the average power of alpha/theta and alpha/delta rhythms in the frontal and temporal leads were calculated.Results. Average power of the alpha-rhythm was significantly higher over the right frontal lobe than over the right frontal-temporal area, as well as average amplitude of it was significantly higher in F3-A1 than F7-A1, F4-A2 than F8-A2, which is associated with the articulatory praxis. Average alpha-rhythm power was significantly higher in T5-A1 than T3-A1 and T6-A2 than T4-A2, which corresponds to the recognition and naming of objects optically. Significant differences according to the total average power of the alpha- and theta-rhythms above the frontal and frontal-temporal regions reflect the relationship between the frontal cortex temporal lobes and the premotor zones, i.e. arcuate bundle, responsible for the “speech system”.Conclusion. The identified patterns can reflect the characteristics of the state of active wakefulness in a 3–4-year-old child and can be used for comparison in the future (both in the course of behavioral experiments and observation of patients with certain pathological processes).

EEG Autocorrelation Analysis of Neuronal Population at Criticality

2013 ◽  
Vol 482 ◽  
pp. 363-366 ◽  
Author(s):  
Zhen Wu ◽  
Xiao Fei Xia ◽  
Jun Song Wang
Keyword(s):  
Information Transmission ◽  
Autocorrelation Function ◽  
Resting State ◽  
Computational Study ◽  
Neuronal Population ◽  
Transmission Efficiency ◽  
Bifurcation Curve ◽  
Autocorrelation Analysis ◽  
Eeg Signals ◽  
The Brain

The brain operates at criticality not only at resting state but also with some recognition tasks. Researches have shown that the information transmission efficiency is maximized when brain operates at criticality, however, the underlined mechanism remains some unknown. In this study, we elucidate the underlined mechanism of neuronal information transmission at criticality through a computational study. Firstly, a bifurcation analysis is conducted, by which we can obtain the Hopf bifurcation curve, responding the critical state. Secondly, we compute the autocorrelation function of the EEG (Electroencephalography) signals. The results have demonstrated that the autocorrelation function at criticality decay slowly and is much larger than other states, meaning long range dependence of the EEG signals at criticality, which reveals that the large autocorrelation function results that the information transmission efficiency is maximized at criticality.

Analysis of Design Activities Using EEG Signals

2010 ◽  
Author(s):  
Thanh An Nguyen ◽  
Yong Zeng
Keyword(s):  
Design Process ◽  
Design Theory ◽  
Eeg Signals ◽  
Solution Generation ◽  
Power Spectral ◽  
Design Activities ◽  
The Subject ◽  
Design Theory And Methodology ◽  
The Given ◽  
The Brain

It plays a significant role in developing of design theory and methodology to understand designer’s thinking and cognitive process during design activities. The most dominant method to conduct this kind of study is protocol analysis. However, this method is prone to subjective factors. Therefore, other approaches are emerging, which can measure the brain activities directly. With the advances in technologies, brain scanner and brain recorder systems such as EEG, fMRI, PET have become more affordable. In the present research, we used EEG to record designer’s brain electrical signals when s/he was working on a design task. Six channels of the EEG signals were recorded, including Fp1, Fp2, Fz, Cz, Pz, Oz, based on which the power spectral density for each EEG band (delta, theta, alpha and beta) was calculated. The results showed that, for the given design problem, the subject spent more effort in visual thinking during the solution generation than that in solution evaluation. The preliminary success in identifying regularity underlying a single designer’s design process through EEG signals lays a foundation for further investigation of designers’ general mental efforts during the conceptual design process.

scholarly journals Classification of Lactate Level Using Resting-State EEG Measurements

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Saad Abdulazeez Shaban ◽  
Osman Nuri Ucan ◽  
Adil Deniz Duru
Keyword(s):  
Feature Extraction ◽  
Resting State ◽  
Acute Exercise ◽  
Enzyme Level ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Information Dynamics ◽  
Feature Extraction Method ◽  
The Brain

The electroencephalography (EEG) signals have been used widely for studying the brain neural information dynamics and behaviors along with the developing impact of using the machine and deep learning techniques. This work proposes a system based on the fast Fourier transform (FFT) as a feature extraction method for the classification of human brain resting-state electroencephalography (EEG) recorded signals. In the proposed system, the FFT method is applied on the resting-state EEG recordings and the corresponding band powers were calculated. The extracted relative power features are supplied to the classification methods (classifiers) as an input for the classification purpose as a measure of human tiredness through predicting lactate enzyme level, high or low. To validate the suggested method, we used an EEG dataset which has been recorded from a group of elite-level athletes consisting of two classes: not tired, the EEG signals were recorded during the resting-state task before performing acute exercise and tired, the EEG signals were recorded in the resting-state after performing an acute exercise. The performance of three different classifiers was evaluated with two performance measures, accuracy and precision values. The accuracy was achieved above 98% by the K-nearest neighbor (KNN) classifier. The findings of this study indicated that the feature extraction scheme has the ability to classify the analyzed EEG signals accurately and predict the level of lactate enzyme high or low. Many studying fields, like the Internet of Things (IoT) and the brain computer interface (BCI), can utilize the findings of the proposed system in many crucial decision-making applications.

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