Fractal Dimension as Quantifier of EEG Activity in Driving Simulation

Dynamical systems and fractal theory methodologies have been proved useful for the modeling and analysis of experimental datasets and, in particular, for electroencephalographic signals. The computation of the fractal dimension of approximation curves in the plane enables the assignment of numerical values to bioelectric recordings in order to discriminate between different states of the observed system. The procedure does not require the stationarity of the signals nor extremely long segments of data. In previous works, we checked that this parameter is a good index for brain activity. In this paper, we consider this measurement in order to quantify the geometric complexity of the brain waves in states of rest and during vehicle driving simulation in different scenarios. This work presents evidence that the fractal dimension allows the detection of the brain bioelectric changes produced in the areas that carry out the different driving simulation tasks, increasing with their complexity.

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ESTIMATING OF BRAIN DEVELOPMENT IN NEWBORNS BY FRACTAL ANALYSIS OF SLEEP ELECTROENCEPHALOGRAPHIC (EEG) SIGNAL

Fractals ◽

10.1142/s0218348x1950021x ◽

2019 ◽

Vol 27 (03) ◽

pp. 1950021 ◽

Cited By ~ 21

Author(s):

HAMIDREZA NAMAZI ◽

SAJAD JAFARI

Keyword(s):

Fractal Dimension ◽

Brain Development ◽

Complexity Analysis ◽

Imaging Techniques ◽

Brain Activity ◽

Fractal Theory ◽

Eeg Signal ◽

Nonlinear Structure ◽

Research Areas ◽

The Brain

Analysis of the brain development is one of the major research areas in human neuroscience. In order to analyze the human brain development, scientists employ different brain imaging techniques. One of the typical techniques to measure the brain activity is electroencephalography (EEG). In this paper, we do complexity analysis on the EEG signal recorded from the newborns during their sleep, in different weeks of post conception. We analyze how the nonlinear structure of EEG signal changes for newborns with their ages by using fractal theory. The result of our analysis showed that the EEG signals for newborn in 45 weeks have the highest fractal dimension. The lowest fractal dimension of EEG signal was obtained for newborns in 36 weeks. Based on our analysis, we conclude that the complexity of brain signal significantly changes with the newborn age. The proposed method is not limited to the analysis of the brain development, and can be applied to investigate the brain activity in different tasks.

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Human Emotion Detection using Eeg Sensor

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.e9326.069520 ◽

2020 ◽

Vol 9 (5) ◽

pp. 409-413

Keyword(s):

Data Visualization ◽

Concentration Level ◽

Brain Activity ◽

Emotion Detection ◽

Attention Span ◽

Brain Waves ◽

Alpha Beta ◽

The Right ◽

The Waves ◽

The Brain

This is a data visualization art piece using 10 seconds of mind waves recordings of the human, captured with EEG sensor.10 seconds of Alpha, Beta, Gamma & Theta brain waves while meditating are recorded, the different wave channels are categorized to state when the right brain representing artistic brain activity, isolating the ranges for each channel when the brain channels were more meditating and imaginative. Based on the waves of the brain obtained, we will be able to deduce few attributes such as attention span and mood. The moods we will be trying to assess and display here the level of happiness, sadness, anger along with attention span and meditation level (Concentration level).

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Serious Games as Potential Therapies: A Validation Study of a Neurofeedback Game

Clinical EEG and Neuroscience ◽

10.1177/1550059419869471 ◽

2019 ◽

Vol 51 (2) ◽

pp. 87-93

Author(s):

Femke Coenen ◽

Floortje E. Scheepers ◽

Saskia J. M. Palmen ◽

Maretha V. de Jonge ◽

Bob Oranje

Keyword(s):

Health Care ◽

Validation Study ◽

Serious Games ◽

Brain Activity ◽

Healthy Male ◽

Eeg Activity ◽

Face To Face ◽

Amplitude Data ◽

The Brain ◽

Concentration Condition

Serious (biofeedback) games offer promising ways to supplement or replace more expensive face-to-face interventions in health care. However, studies on the validity and effectiveness of EEG-based serious games remain scarce. In the current study, we investigated whether the conditions of the neurofeedback game “Daydream” indeed trained the brain activity as mentioned in the game manual. EEG activity was assessed in 14 healthy male volunteers while playing the 2 conditions of the game. The participants completed a training of 5 sessions. EEG frequency analyses were performed to verify the claims of the manual. We found significant differences in α- to β-ratio between the 2 conditions although only in the amplitude data, not in the power data. Within the conditions, mean α-amplitude only differed significantly from the β-amplitude in the concentration condition. Our analyses showed that neither α nor β brain activity differed significantly between game levels (higher level requiring increased brain activity) in either of the two conditions. In conclusion, we found only marginal evidence for the proposed claims stated in the manual of the game. Our research emphasizes that it is crucial to validate the claims that serious games make, especially before implementing them in the clinic or as therapeutic devices.

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Deep Learning on Game Addiction Detection Based on Electroencephalogram

JURNAL MEDIA INFORMATIKA BUDIDARMA ◽

10.30865/mib.v5i3.3061 ◽

2021 ◽

Vol 5 (3) ◽

pp. 963

Author(s):

Lalu Arfi Maulana Pangistu ◽

Ahmad Azhari

Keyword(s):

Brain Activity ◽

Late Adolescence ◽

Wave Activity ◽

Test Results ◽

Brain Wave ◽

Brain Waves ◽

Game Addiction ◽

Brain Wave Activity ◽

Electroencephalogram Eeg ◽

The Brain

Playing games for too long can be addictive. Based on a recent study by Brand et al, adolescents are considered more vulnerable than adults to game addiction. The activity of playing games produces a wave in the brain, namely beta waves where the person is in a focused state. Brain wave activity can be measured and captured using an Electroencephalogram (EEG). Recording brain wave activity naturally requires a prominent and constant brain activity such as when concentrating while playing a game. This study aims to detect game addiction in late adolescence by applying Convolutional Neural Network (CNN). Recording of brain waves was carried out three times for each respondent with a stimulus to play three different games, namely games included in the easy, medium, and hard categories with a consecutive taking time of 10 minutes, 15 minutes, and 30 minutes. Data acquisition results are feature extraction using Fast Fourier Transform to get the average signal for each respondent. Based on the research conducted, obtained an accuracy of 86% with a loss of 0.2771 where the smaller the loss value, the better the CNN model built. The test results on the model produce an overall accuracy of 88% with misclassification in 1 data. The CNN model built is good enough for the detection of game addiction in late adolescence.

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DECODING OF SIMPLE AND COMPOUND LIMB MOTOR IMAGERY MOVEMENTS BY FRACTAL ANALYSIS OF ELECTROENCEPHALOGRAM (EEG) SIGNAL

Fractals ◽

10.1142/s0218348x19500415 ◽

2019 ◽

Vol 27 (03) ◽

pp. 1950041 ◽

Cited By ~ 13

Author(s):

HAMIDREZA NAMAZI ◽

TIRDAD SEIFI ALA

Keyword(s):

Fractal Dimension ◽

Motor Imagery ◽

Fractal Analysis ◽

Brain Activity ◽

Eeg Signal ◽

Left Hand ◽

Right Hand ◽

Significant Difference ◽

Electroencephalogram Eeg ◽

The Brain

One of the major attempts in rehabilitation science is to decode different movements of human using physiological signals. Since human movements are mainly controlled by the brain, decoding of movements by analysis of the brain activity has great importance. In this paper, we apply fractal analysis to Electroencephalogram (EEG) signal in order to decode simple and compound limb motor imagery movements. The fractal dimension of EEG signal is analyzed in case of left hand, right hand, both hands, feet, left hand combined with right foot, and right hand combined with left foot movements. Based on the obtained results, EEG signal experiences the lowest and greatest fractal dimension in case of both hands movement, and feet movement, respectively. Besides obtaining different fractal dimension for EEG signal in case of different movements, no significant difference was observed in fractal dimension of EEG signal between different movements. The method of analysis employed in this research can be widely applied to analysis of EEG signal for decoding of different movements of human.

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Introduction to the identification of brain waves based on their frequency

MATEC Web of Conferences ◽

10.1051/matecconf/201821005012 ◽

2018 ◽

Vol 210 ◽

pp. 05012 ◽

Cited By ~ 1

Author(s):

Zuzana Koudelková ◽

Martin Strmiska

Keyword(s):

Brain Activity ◽

Computer Interface ◽

Brain Wave ◽

Non Invasive ◽

Brain Waves ◽

Analytical Reasoning ◽

Brain Signals ◽

Electroencephalogram Eeg ◽

The Brain ◽

Research Type

A Brain Computer Interface (BCI) enables to get electrical signals from the brain. In this paper, the research type of BCI was non-invasive, which capture the brain signals using electroencephalogram (EEG). EEG senses the signals from the surface of the head, where one of the important criteria is the brain wave frequency. This paper provides the measurement of EEG using the Emotiv EPOC headset and applications developed by Emotiv System. Two types of the measurements were taken to describe brain waves by their frequency. The first type of the measurements was based on logical and analytical reasoning, which was captured during solving mathematical exercise. The second type was based on relax mind during listening three types of relaxing music. The results of the measurements were displayed as a visualization of a brain activity.

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Analysis of task-based functional MRI data preprocessed with fMRIPrep

10.1101/694364 ◽

2019 ◽

Cited By ~ 2

Author(s):

Oscar Esteban ◽

Rastko Ciric ◽

Karolina Finc ◽

Ross Blair ◽

Christopher J. Markiewicz ◽

...

Keyword(s):

Ad Hoc ◽

Brain Activity ◽

Imaging Data ◽

Modeling And Analysis ◽

Manual Intervention ◽

Standard Tool ◽

History Of ◽

Brain Imaging Data ◽

Many Sources ◽

The Brain

Functional magnetic resonance imaging (fMRI) is a standard tool to investigate the neural correlates of cognition. fMRI noninvasively measures brain activity, allowing identification of patterns evoked by tasks performed during scanning. Despite the long history of this technique, the idiosyncrasies of each dataset have led to the use of ad-hoc preprocessing protocols customized for nearly every different study. This approach is time-consuming, error-prone, and unsuitable for combining datasets from many sources. Here we showcase fMRIPrep (http://fmriprep.org), a robust tool to prepare human fMRI data for statistical analysis. This software instrument addresses the reproducibility concerns of the established protocols for fMRI preprocessing. By leveraging the Brain Imaging Data Structure (BIDS) to standardize both the input datasets —MRI data as stored by the scanner— and the outputs —data ready for modeling and analysis—, fMRIPrep is capable of preprocessing a diversity of datasets without manual intervention. In support of the growing popularity of fMRIPrep, this protocol describes how to integrate the tool in a task-based fMRI investigation workflow.

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ANALYSIS OF THE CORRELATION BETWEEN EYES AND BRAIN ACTIVITIES IN RESPONSE TO MOVING VISUAL STIMULI

Fractals ◽

10.1142/s0218348x21502741 ◽

2021 ◽

Author(s):

RAMESH RAMAMOORTHY ◽

AVINASH MENON ◽

KARTHIKEYAN RAJAGOPAL ◽

ROBERT FRISCHER ◽

HAMIDREZA NAMAZI

Keyword(s):

Fractal Dimension ◽

Eye Movements ◽

Fractal Theory ◽

Visual Stimuli ◽

Sample Entropy ◽

Eeg Signals ◽

Complex Patterns ◽

The Brain

This paper analyzed the coupling among the reactions of eyes and brain in response to visual stimuli. Since eye movements and electroencephalography (EEG) signals as the features of eye and brain activities have complex patterns, we utilized fractal theory and sample entropy to decode the correlation between them. In the experiment, subjects looked at a dot that moved on different random paths (dynamic visual stimuli) on the screen of a computer in front of them while we recorded their EEG signals and eye movements simultaneously. The results indicated that the changes in the complexity of eye movements and EEG signals are coupled ([Formula: see text] in case of fractal dimension and [Formula: see text] in case of sample entropy), which reflects the coupling between the brain and eye activities. This analysis could be extended to evaluate the correlation between the activities of other organs versus the brain.

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Fractal Dimension of Cortical Functional Connectivity Networks Predicts Severity in Disorders of Consciousness

10.1101/789636 ◽

2019 ◽

Author(s):

TF. Varley ◽

M. Craig ◽

R. Adapa ◽

P. Finoia ◽

G. Williams ◽

...

Keyword(s):

Fractal Dimension ◽

Functional Connectivity ◽

Healthy Volunteers ◽

Critical Point ◽

Brain Activity ◽

Conscious Experience ◽

Minimally Conscious State ◽

Disorders Of Consciousness ◽

Level Of Consciousness ◽

The Brain

AbstractRecent evidence suggests that the quantity and quality of conscious experience may be a function of the complexity of activity in the brain, and that consciousness emerges in a critical zone on the axes of order/randomness and integration/differentiation. We propose fractal shapes as a measure of proximity to this critical point, as fractal dimension encodes information about complexity beyond simple entropy or randomness, and fractal structures are known to emerge in systems nearing a critical point. To validate this, we tested the several measures of fractal dimension on the brain activity from healthy volunteers and patients with disorders of consciousness of varying severity. We used a Compact Box Burning algorithm to compute the fractal dimension of cortical functional connectivity networks as well as computing the fractal dimension of the associated adjacency matrices using a 2D box-counting algorithm. To test whether brain activity is fractal in time as well as space, we used the Higuchi temporal fractal dimension on BOLD time-series. We found significant decreases in the fractal dimension between healthy volunteers (n=15), patients in a minimally conscious state (n=10), and patients in a vegetative state (n=8), regardless of the mechanism of injury. We also found significant decreases in adjacency matrix fractal dimension and Higuchi temporal fractal dimension, which correlated with decreasing level of consciousness. These results suggest that cortical functional connectivity networks display fractal character and that this is predictive of level of consciousness in a clinically relevant population, with more fractal (i.e. more complex) networks being associated with higher levels of consciousness. This supports the hypothesis that level of consciousness and system complexity are positively associated, and is consistent with previous EEG, MEG, and fMRI studies.

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Cerebral Activation Patterns Of Older Adults While Performing The Functional Gait Test

10.21203/rs.3.rs-16510/v1 ◽

2020 ◽

Author(s):

Larissa Bastos Tavares ◽

Idaliana Fagundes de Souza ◽

Bartolomeu Fagundes de Lima Filho ◽

Kim Mansur Yano ◽

Juliana Maria Gazzola ◽

...

Keyword(s):

Older Adults ◽

Dual Task ◽

Right Hemisphere ◽

Brain Activity ◽

Complex Task ◽

Simple Task ◽

Brain Waves ◽

The Right ◽

Functional Gait ◽

The Brain

Abstract Dual-task activities are common in daily life and have greater motor/cognitive demands. These are conditions that increase the risk of older adult falls. Falls are a public health problem. Brain mapping during dual-task activities can inform which therapeutic activities stimulate specific brain areas, improving functionality, and decreasing dependence and the risk of falls. The objective of the study was to characterize the brain activity of healthy older adults while performing a dual-task activity called the Functional Gait Test (FGT). Method : This observational study included 30 older adults aged 65 to 75 years, and it was approved by the institutional review board. The FGT consists of walking following a sequence of numbers (simple task), and a sequence of alternating letters and numbers (complex task). During the activity, the subjects had their cortical activation pattern measured using the Emotiv EPOC® electroencephalogram. Complete data was obtained for analysis on 13 participants. The data was analyzed using descriptive statistics (mean and standard deviation), and paired T-tests to compare the brain activity during the conditions (simple vs. complex task). Results : Alpha brain waves were activated in the right and left hemispheres during the simple task, while Alpha brain waves’ activation during the complex task was predominant in the right hemisphere. However, the differences were not statistically significant. The Betha waves had predominant activation in the left hemisphere during the simple task, and predominant activation in the right hemisphere during the complex task. The difference was statistically significant in 11 out of the 14 channels evaluated ( P <0.04). Conclusion: The results corroborates the increased complexity of dual-tasks due to the predominant activation of the right hemisphere, which is related to motor learning process and new stimulus processing.

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