scholarly journals Changes in the Electrical Activity of the Brain in the Alpha and Theta Bands during Prayer and Meditation

2020 ◽  
Vol 17 (24) ◽  
pp. 9567
Author(s):  
Paweł Dobrakowski ◽  
Michal Blaszkiewicz ◽  
Sebastian Skalski
Keyword(s):  
Neural Activity ◽  
Emotional Experience ◽  
Focused Attention ◽  
Eeg Signal ◽  
Alpha Wave ◽  
Spiritual Transcendence ◽  
Eyes Closed ◽  
Activity Integration ◽  
The Brain ◽  
Eeg Pattern

Focused attention meditation (FAM) is a category of meditation based on an EEG pattern, which helps the wandering mind to focus on a particular object. It seems that prayer may, in certain respects, be similar to FAM. It is believed that emotional experience correlates mainly with theta, but also with selective alpha, with internalized attention correlating mainly with the synchronous activity of theta and alpha. The vast majority of studies indicate a possible impact of transcendence in meditation on the alpha wave in EEG. No such reports are available for prayer. Seventeen women and nineteen men aged 27–64 years with at least five years of intensive meditation/prayer experience were recruited to participate in the study. We identified the two largest groups which remained in the meditation trend originating from the Buddhist system (14 people) (Buddhist meditators) and in the Christian-based faith (15 people) (Christian meditators). EEG signal was recorded with open eyes, closed eyes, during meditation/prayer, and relaxation. After the EEG recording, an examination was conducted using the Scale of Spiritual Transcendence. Buddhist meditators exhibited a statistically significantly higher theta amplitude at Cz during meditation compared to relaxation. Meanwhile, spiritual openness favored a higher theta amplitude at Pz during relaxation. Our study did not reveal statistically significant differences in frontal areas with regard to alpha and theta, which was often indicated in previous studies. It seems necessary to analyze more closely the midline activity in terms of dispersed neural activity integration.

scholarly journals The neural correlates of two forms of spiritual love: an EEG study

2016 ◽  
Author(s):  
E. S. Louise Faber
Keyword(s):  
Neural Activity ◽  
Emotional Experience ◽  
Gamma Oscillations ◽  
Neural Correlates ◽  
Spiritual Practices ◽  
Loving Kindness ◽  
Beta Power ◽  
Delta Activity ◽  
Gamma Power ◽  
The Brain

AbstractSpiritual practices are gaining an increasingly wider audience as a means to enhance positive affect in healthy individuals and to treat neurological disorders such as anxiety and depression. The current study aimed to examine the neural correlates of two different forms of love generated by spiritual practices using EEG; love generated during a loving kindness meditation performed by Buddhist meditators, and love generated during prayer, in a separate group of participants from a Christian-based faith. The loving kindness meditation was associated with significant increases in delta, alpha 1, alpha 2 and beta power compared to baseline, while prayer induced significant increases in power of alpha 1 and gamma oscillations, together with an increase in the gamma: theta ratio. An increase in delta activity occurred during the loving kindness meditation but not during prayer. In contrast increases in theta, alpha 1, alpha 2, beta and gamma power were observed when comparing both types of practice to baseline, suggesting that increases in these frequency bands are the neural correlates of spiritual love, independent of the type of practice used to attain the state of this type of love. These findings show that both spiritual love practices are associated with widespread changes in neural activity across the brain, in particular at frequency ranges that have been implicated in positive emotional experience, integration of distributed neural activity, and changes in short-term and longterm neural circuitry.

scholarly journals The Significance of Delta Wave Among Athletes

2018 ◽  
Vol 7 (3.18) ◽  
pp. 7
Author(s):  
Norsiah Fauzan ◽  
Nor Mazlina Ghazali
Keyword(s):  
Stroop Task ◽  
Gamma Oscillations ◽  
Peak Performance ◽  
Quantitative Electroencephalography ◽  
Alpha Wave ◽  
Sports Activity ◽  
Eyes Closed ◽  
Visual Spatial ◽  
Task Conditions ◽  
The Brain

This article reports on the differences between the physiological response of the brain between athletes and non-athletes by using Quantitative Electroencephalography (QEEG). EEG waves were observed using qEEG and analysis were compared between the two groups.  This research involved 41 undergraduates of Universiti Malaysia Sarawak (UNIMAS). The qEEG recordings were made during the Eyes opened, Eyes Closed, and Stroop task conditions to find out the dominant wave during each of the conditions in different region of the brain. The results revealed higher EEG Delta and Beta1 at frontal region (Fp1, Fp2), somatosensory, (C3, P4) and visual spatial area (P3, P4). Delta, Beta and Gamma wave were dominant while the participants were performing the Stroop Task. Coupling of delta and beta oscillations might be due to the athletes’ anxiety during the Stroop task.  In Eyes Closed state, delta and alpha wave were dominant at the fronto-parietal attention network area. This study contributes to the development of training protocol for neurofeedback training for athletes in preparation for training of peak performance in any sports activity. It is recommended that extensive analysis should be done on the interaction of delta-gamma oscillations in different parts of the brain to find out its implication on attention and emotion during the cognitive process.  

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

From many to one: Meditation and the plasticity of the predictive mind

2020 ◽  
Author(s):  
Ruben Laukkonen ◽  
Heleen A Slagter
Keyword(s):  
Cognitive Enhancement ◽  
The Self ◽  
Predictive Processing ◽  
Focused Attention ◽  
Conceptual Processing ◽  
Current State ◽  
Contemplative Science ◽  
Open Monitoring ◽  
Living Organisms ◽  
The Brain

How profoundly can humans change their own minds? In this paper we offer a unifying account of meditation under the predictive processing view of living organisms. We start from relatively simple axioms. First, the brain is an organ that serves to predict based on past experience, both phylogenetic and ontogenetic. Second, meditation serves to bring one closer to the here and now by disengaging from anticipatory processes. We propose that practicing meditation therefore gradually reduces predictive processing, in particular counterfactual cognition—the tendency to construct abstract and temporally deep representations—until all conceptual processing falls away. Our Many- to-One account also places three main styles of meditation (focused attention, open monitoring, and non-dual meditation) on a single continuum, where each technique progressively relinquishes increasingly engrained habits of prediction, including the self. This deconstruction can also make the above processes available to introspection, permitting certain insights into one’s mind. Our review suggests that our framework is consistent with the current state of empirical and (neuro)phenomenological evidence in contemplative science, and is ultimately illuminating about the plasticity of the predictive mind. It also serves to highlight that contemplative science can fruitfully go beyond cognitive enhancement, attention, and emotion regulation, to its more traditional goal of removing past conditioning and creating conditions for potentially profound insights. Experimental rigor, neurophenomenology, and no-report paradigms combined with neuroimaging are needed to further our understanding of how different styles of meditation affect predictive processing and the self, and the plasticity of the predictive mind more generally.

SIMULATION AND MODELING OF EVOKED RESPONSE ELECTROENCEPHALOGRAPH SIGNAL

1999 ◽  
Vol 10 (04) ◽  
pp. 759-776
Author(s):  
D. R. KULKARNI ◽  
J. C. PARIKH ◽  
R. PRATAP
Keyword(s):  
Small Range ◽  
Eeg Signal ◽  
Simulation And Modeling ◽  
State Space Reconstruction ◽  
Eyes Closed ◽  
Normal Individuals ◽  
Eeg Data ◽  
Closed Data ◽  
Simulation Results ◽  
Low Dimensional

Electroencephalograph (EEG) data for normal individuals with eyes-closed and under stimuli is analyzed. The stimuli consisted of photo, audio, motor and mental activity. We use several measures from nonlinear dynamics to analyze and characterize the data. We find that the dynamics of the EEG signal is deterministic and chaotic but it is not a low dimensional chaotic system. The evoked responses lead to a redistribution of strengths relative to eyes-closed data. Basically, strength in α waves decreases whereas that in β wave increases. We also carried out simulations separately and in combination for δ, θ, α and β waves to understand the data. From the simulation results, it appears that the characteristics of EEG data are consequences of filtering the data with a relatively small range of frequency (0.5–32 Hz). In view of this, we believe that calculation of known nonlinear measures is not likely to be very useful for studying the dynamics of EEG data. We have also successfully modeled the EEG time series using the concept of state space reconstruction in the framework of artificial neural network. It gives us confidence that one would be able to understand, in a more basic way, how collectivity in EEG signal arises.

scholarly journals Harmonic Brain Modes: A Unifying Framework for Linking Space and Time in Brain Dynamics

2017 ◽  
Vol 24 (3) ◽  
pp. 277-293 ◽  
Author(s):  
Selen Atasoy ◽  
Gustavo Deco ◽  
Morten L. Kringelbach ◽  
Joel Pearson
Keyword(s):  
Neural Activity ◽  
Brain Activity ◽  
Activity Patterns ◽  
Structural Connectivity ◽  
Building Blocks ◽  
Mental States ◽  
Brain Dynamics ◽  
Space And Time ◽  
Wide Range ◽  
The Brain

A fundamental characteristic of spontaneous brain activity is coherent oscillations covering a wide range of frequencies. Interestingly, these temporal oscillations are highly correlated among spatially distributed cortical areas forming structured correlation patterns known as the resting state networks, although the brain is never truly at “rest.” Here, we introduce the concept of harmonic brain modes—fundamental building blocks of complex spatiotemporal patterns of neural activity. We define these elementary harmonic brain modes as harmonic modes of structural connectivity; that is, connectome harmonics, yielding fully synchronous neural activity patterns with different frequency oscillations emerging on and constrained by the particular structure of the brain. Hence, this particular definition implicitly links the hitherto poorly understood dimensions of space and time in brain dynamics and its underlying anatomy. Further we show how harmonic brain modes can explain the relationship between neurophysiological, temporal, and network-level changes in the brain across different mental states ( wakefulness, sleep, anesthesia, psychedelic). Notably, when decoded as activation of connectome harmonics, spatial and temporal characteristics of neural activity naturally emerge from the interplay between excitation and inhibition and this critical relation fits the spatial, temporal, and neurophysiological changes associated with different mental states. Thus, the introduced framework of harmonic brain modes not only establishes a relation between the spatial structure of correlation patterns and temporal oscillations (linking space and time in brain dynamics), but also enables a new dimension of tools for understanding fundamental principles underlying brain dynamics in different states of consciousness.

Shape Memory Alloy Actuatable Woven Neural Probes

2018 ◽  
Author(s):  
Gabriella Shull ◽  
Jay Jia Hu ◽  
Justin Buschnyj ◽  
Henry Koon ◽  
Julianna Abel ◽  
...  
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Neural Activity ◽  
Inflammatory Responses ◽  
Neural Probes ◽  
Thermally Induced ◽  
Tissue Phantom ◽  
Computer Interfaces ◽  
Ordered Array ◽  
The Brain

The ability to sense neural activity using electrodes has allowed scientists to use this information to temporarily restore movement in paralyzed individuals using brain-computer interfaces (BCI). However, current electrodes do not provide chronic recording of the brain due to the inflammatory response of the immune system caused by the large (∼ 20–80 μm) size of the shanks, and the mechanical mismatch of the shanks relative to the brain. Electrode designs are evolving to use small (< 15 μm) flexible neural probes to minimize inflammatory responses and enable chronic use. However, their flexibility limits the scalability — it is challenging to assemble 3D arrays of such electrodes, to insert the arrays of flexible neural probes into the brain without buckling, and to uniformly distribute them into large areas of the brain. Thus, we created Shape Memory Alloy (SMA) actuated Woven Neural Probes (WNPs). A linear array of 32 flexible insulated microwires were interwoven with SMA wires resulting in an ordered array of parallel electrodes. SMA WNPs were shaped to an initial constricted profile for reliable insertion into a tissue phantom. Following insertion, the SMA wires were used as actuators to unravel the constricted WNP to distribute electrodes across large volumes. We demonstrated that the WNPs could be inserted into the brain without buckling and record neural activity. In separate experiments, we showed that the SMA could mechanically distribute the WNPs via thermally induced actuation. This work thus highlights the potential of actuatable WNPs to be used as a platform for neural recording.

scholarly journals Analytical Comparison of Two Emotion Classification Models Based on Convolutional Neural Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Huiping Jiang ◽  
Demeng Wu ◽  
Rui Jiao ◽  
Zongnan Wang
Keyword(s):  
Wavelet Transform ◽  
Classification Accuracy ◽  
Eeg Signal ◽  
Classification Models ◽  
Emotion Classification ◽  
Common Space ◽  
Average Classification Accuracy ◽  
The Common ◽  
The Brain

Electroencephalography (EEG) is the measurement of neuronal activity in different areas of the brain through the use of electrodes. As EEG signal technology has matured over the years, it has been applied in various methods to EEG emotion recognition, most significantly including the use of convolutional neural network (CNN). However, these methods are still not ideal, and shortcomings have been found in the results of some models of EEG feature extraction and classification. In this study, two CNN models were selected for the extraction and classification of preprocessed data, namely, common spatial patterns- (CSP-) CNN and wavelet transform- (WT-) CNN. Using the CSP-CNN, we first used the common space model to reduce dimensionality and then applied the CNN directly to extract and classify the features of the EEG; while, with the WT-CNN model, we used the wavelet transform to extract EEG features, thereafter applying the CNN for classification. The EEG classification results of these two classification models were subsequently analyzed and compared, with the average classification accuracy of the CSP-CNN model found to be 80.56%, and the average classification accuracy of the WT-CNN model measured to 86.90%. Thus, the findings of this study show that the average classification accuracy of the WT-CNN model was 6.34% higher than that of the CSP-CNN.

scholarly journals Neural Mass Model-Based Different EEG Signal Generation and Analysis in Simulink Sheikh Md. Rabiul Islam1, Md. Shakibul Islam2

2021 ◽  
pp. 1-7
Author(s):  
Sheikh Md. Rabiul Islam ◽  
◽  
Md. Shakibul Islam ◽  
Keyword(s):  
Signal Generation ◽  
Neural Mass Model ◽  
Pathophysiological Mechanism ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Mass Model ◽  
Detailed Model ◽  
Simulation Based ◽  
Neural Mass ◽  
The Brain

The electroencephalogram (EEG) is an electrophysiological monitoring strategy that records the spontaneous electrical movement of the brain coming about from ionic current inside the neurons of the brain. The importance of the EEG signal is mainly the diagnosis of different mental and brain neurodegenerative diseases and different abnormalities like seizure disorder, encephalopathy, dementia, memory problem, sleep disorder, stroke, etc. The EEG signal is very useful for someone in case of a coma to determine the level of brain activity. So, it is very important to study EEG generation and analysis. To reduce the complexity of understanding the pathophysiological mechanism of EEG signal generation and their changes, different simulation-based EEG modeling has been developed which are based on anatomical equivalent data. In this paper, Instead of a detailed model a neural mass model has been used to implement different simulation-based EEG models for EEG signal generation which refers to the simplified and straightforward method. This paper aims to introduce obtained EEG signals of own implementation of the Lopes da Silva model, Jansen-Rit model, and Wendling model in Simulink and to compare characteristic features with real EEG signals and better understanding the EEG abnormalities especially the seizure-like signal pattern.

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