scholarly journals Fractal dimension of EEG signal senses complexity of fractal animations

2021 ◽  
Author(s):  
Sarshar Dorosti ◽  
Reza Khosrowabadi
Keyword(s):  
Fractal Dimension ◽  
Information Processing ◽  
Visual Stimuli ◽  
Fractal Dimensions ◽  
Neural Mechanism ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Spatial And Temporal Scales ◽  
Box Counting Method ◽  
The Brain

AbstractWe are surrounded with many fractal and self-similar patterns which has been area of many researches in the recent years. We can perceive self-similarities in various spatial and temporal scales; however, the underlying neural mechanism needs to be well understood. In this study, we hypothesized that complexity of visual stimuli directly influence complexity of information processing in the brain. Therefore, changes in fractal pattern of EEG signal must follow change in fractal dimension of animation. To investigate this hypothesis, we recorded EEG signal of fifteen healthy participants while they were exposed to several 2D fractal animations. Fractal dimension of each frame of the animation was estimated by box counting method. Subsequently, fractal dimensions of 32 EEG channels were estimated in a frequency specific manner. Then, association between pattern of fractal dimensions of the animations and pattern of fractal dimensions of EEG signals were calculated using the Pearson’s correlation algorithm. The results indicated that fractal animation complexity is mainly sensed by changes in fractal dimension of EEG signals at the centro-parietal and parietal regions. It may indicate that when the complexity of visual stimuli increases the mechanism of information processing in the brain also enhances its complexity to better attend and comprehend the stimuli.

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

Fractals ◽  
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.

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.

AGE-BASED VARIATIONS OF FRACTAL STRUCTURE OF EEG SIGNAL IN PATIENTS WITH EPILEPSY

Fractals ◽  
2018 ◽  
Vol 26 (04) ◽  
pp. 1850051 ◽  
Author(s):  
HAMIDREZA NAMAZI ◽  
SAJAD JAFARI
Keyword(s):  
Fractal Dimension ◽  
Brain Research ◽  
Age Groups ◽  
Eeg Signal ◽  
Brain Disorders ◽  
Eeg Signals ◽  
Important Challenge ◽  
Patients With Epilepsy ◽  
Process Complexity ◽  
Electroencephalogram Eeg

It is known that aging affects neuroplasticity. On the other hand, neuroplasticity can be studied by analyzing the electroencephalogram (EEG) signal. An important challenge in brain research is to study the variations of neuroplasticity during aging for patients suffering from epilepsy. This study investigates the variations of the complexity of EEG signal during aging for patients with epilepsy. For this purpose, we employed fractal dimension as an indicator of process complexity. We classified the subjects in different age groups and computed the fractal dimension of their EEG signals. Our investigations showed that as patients get older, their EEG signal will be more complex. The method of investigation that has been used in this study can be further employed to study the variations of EEG signal in case of other brain disorders during aging.

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.

Information-based Analysis of the Coupling between Dynamic Visual Stimuli, Eye Movements, and Brain Signals

2021 ◽  
pp. 2150048
Author(s):  
Hamidreza Namazi ◽  
Avinash Menon ◽  
Ondrej Krejcar
Keyword(s):  
Eye Movements ◽  
Moving Objects ◽  
Brain Activity ◽  
Visual Stimuli ◽  
Strongly Correlated ◽  
Eeg Signals ◽  
Vision Science ◽  
Brain Signals ◽  
Information Contents ◽  
The Brain

Our eyes are always in search of exploring our surrounding environment. The brain controls our eyes’ activities through the nervous system. Hence, analyzing the correlation between the activities of the eyes and brain is an important area of research in vision science. This paper evaluates the coupling between the reactions of the eyes and the brain in response to different moving visual stimuli. Since both eye movements and EEG signals (as the indicator of brain activity) contain information, we employed Shannon entropy to decode the coupling between them. Ten subjects looked at four moving objects (dynamic visual stimuli) with different information contents while we recorded their EEG signals and eye movements. The results demonstrated that the changes in the information contents of eye movements and EEG signals are strongly correlated ([Formula: see text]), which indicates a strong correlation between brain and eye activities. This analysis could be extended to evaluate the correlation between the activities of other organs versus the brain.

Cognitive Analytics for Rapid Stress Relief in Humans Using EEG Based Analysis of Tratak Sadhana (Meditation)

2020 ◽  
Vol 10 (4) ◽  
pp. 1-20
Author(s):  
Swati Kamthekar ◽  
Prachi Deshpande ◽  
Brijesh Iyer
Keyword(s):  
Big Data ◽  
Fractal Dimensions ◽  
Approximate Entropy ◽  
Stress Relief ◽  
Eeg Signals ◽  
Nonlinear Parameters ◽  
Wavelet Entropy ◽  
Rest State ◽  
Eyes Closed ◽  
The Brain

The article reports the effect of Tratak Sadhana (meditation) on humans using electroencephalograph (EEG) signals. EEG represents the brain activities in the form of electrical signals. Due to non-stationary nature of the EEG signals, nonlinear parameters like approximate entropy, wavelet entropy and Higuchi' fractal dimensions are used to assess the variations in EEG rest as well as during Tratak Sadhana, i.e. at a rest state with eyes closed and during Tratak meditation. EEG signals are captured using EPOC Emotive EEG sensor. The sensor has 14 electrodes covering human scalp. Results shows that new practitioners can also achieve a rapid meditative state as compared to other meditation techniques. Further, the Big Data perspective of the present study is discussed. The present study shows that Tratak Sadhana meditation is an effective tool for rapid stress relief in humans.

Complexity-Based Analysis of the Relation between Human Muscle Reaction and Walking Path

2020 ◽  
Vol 19 (03) ◽  
pp. 2050025 ◽  
Author(s):  
Shahul Mujib Kamal ◽  
Sue Sim ◽  
Rui Tee ◽  
Visvamba Nathan ◽  
Hamidreza Namazi
Keyword(s):  
Fractal Dimension ◽  
Statistical Analysis ◽  
Fractal Theory ◽  
Contact Point ◽  
Fractal Dimensions ◽  
Human Muscle ◽  
Physiological Signals ◽  
Eeg Signal ◽  
Leg Muscles ◽  
Emg Signal

Legs are the contact point of humans during walking. In fact, leg muscles react when we walk in different conditions (such as different speeds and paths). In this research, we analyze how walking path affects leg muscles’ reaction. In fact, we investigate how the complexity of muscle reaction is related to the complexity of path of movement. For this purpose, we employ fractal theory. In the experiment, subjects walk on different paths that have different fractal dimensions and then we calculate the fractal dimension of Electromyography (EMG) signals obtained from both legs. The result of our analysis showed that the complexity of EMG signal increases with the increment of complexity of path of movement. The conducted statistical analysis also supported the result of analysis. The method of analysis used in this research can be further applied to find the relation between complexity of path of movement and other physiological signals of humans such as respiration and Electroencephalography (EEG) signal.

Mathematical Model Incorporated Fractal Geometry and Permeability of Compressed Peanut and Sesame for Oil Extraction

2012 ◽  
Vol 550-553 ◽  
pp. 676-681
Author(s):  
Xiao Zheng ◽  
Jing Zhou Wang ◽  
Guo Xiang Lin ◽  
Zhi Xian Sun ◽  
Don Ping He
Keyword(s):  
Mathematical Model ◽  
Fractal Dimension ◽  
Applied Pressure ◽  
Fractal Dimensions ◽  
Image Analyzer ◽  
Fractal Characteristic ◽  
Pore Size Distributions ◽  
The Difference ◽  
Sesame Cake ◽  
Box Counting Method

Considering the fractal characteristic of oilseed cake, the relationship between the permeability and the pore fractal dimension of peanut and sesame cake has been investigated. The microstructures of peanut and sesame cake under five applied pressures are measured by using stereo light microscope and Image-pro image analyzer. Using the box-counting method, the fractal dimensions of pore size distributions are measured. A mathematical model incorporated fractal dimension and permeability has been developed to predicate the permeability of compressed peanut and sesame under cold condition based upon combining Hagen-Poiseulle equation with Darcy’s law for flow of fluid through porous media. There is a prediction of permeability of peanut and sesame cake. Thus, a measurement is carried out for validation. The values of mean relative errors are 19.4% and 11.4 respectively. A fairly good agreement is obtained in the case of high applied pressure. And there exists a tendency that the value of the difference between the theoretical calculation and the permeability measurement decrease significantly with the increase of applied pressure.

scholarly journals Study on the Fractal Dimension and Growth Time of the Electrical Treeing Degradation at Different Temperature and Moisture

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Youping Fan ◽  
Dai Zhang ◽  
Jingjiao Li
Keyword(s):  
Fractal Dimension ◽  
Tree Growth ◽  
Fractal Dimensions ◽  
Moisture Level ◽  
Growth Time ◽  
Counting Method ◽  
Box Counting ◽  
Electrical Trees ◽  
Box Counting Method ◽  
Electrical Treeing

The paper aims to understand how the fractal dimension and growth time of electrical trees change with temperature and moisture. The fractal dimension of final electrical trees was estimated using 2-D box-counting method. Four groups of electrical trees were grown at variable moisture and temperature. The relation between growth time and fractal dimension of electrical trees were summarized. The results indicate the final electrical trees can have similar fractal dimensions via similar tree growth time at different combinations of moisture level and temperature conditions.

scholarly journals FRACTAL ANALYSIS OF TRABECULAR BONE: A STANDARDISED METHODOLOGY

2011 ◽  
Vol 19 (1) ◽  
pp. 45 ◽  
Author(s):  
Ian Parkinson ◽  
Nick Fazzalari
Keyword(s):  
Fractal Dimension ◽  
Trabecular Bone ◽  
Fractal Analysis ◽  
Fractal Dimensions ◽  
Box Counting ◽  
Cell Tissue ◽  
Image Analyser ◽  
Model Independent ◽  
Box Counting Method ◽  
Image Orientation

A standardised methodology for the fractal analysis of histological sections of trabecular bone has been established. A modified box counting method has been developed for use on a PC based image analyser (Quantimet 500MC, Leica Cambridge). The effect of image analyser settings, magnification, image orientation and threshold levels, was determined. Also, the range of scale over which trabecular bone is effectively fractal was determined and a method formulated to objectively calculate more than one fractal dimension from the modified Richardson plot. The results show that magnification, image orientation and threshold settings have little effect on the estimate of fractal dimension. Trabecular bone has a lower limit below which it is not fractal (λ<25 μm) and the upper limit is 4250 μm. There are three distinct fractal dimensions for trabecular bone (sectional fractals), with magnitudes greater than 1.0 and less than 2.0. It has been shown that trabecular bone is effectively fractal over a defined range of scale. Also, within this range, there is more than 1 fractal dimension, describing spatial structural entities. Fractal analysis is a model independent method for describing a complex multifaceted structure, which can be adapted for the study of other biological systems. This may be at the cell, tissue or organ level and compliments conventional histomorphometric and stereological techniques.

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