scholarly journals Complexity-Based Analysis of the Variations of Brain and Muscle Reactions in Walking and Standing Balance While Receiving Different Perturbations

2021 ◽  
Vol 15 ◽  
Author(s):  
Najmeh Pakniyat ◽  
Hamidreza Namazi
Keyword(s):  
Virtual Reality ◽  
Fractal Dimension ◽  
Sample Entropy ◽  
Standing Balance ◽  
Physiological Signals ◽  
Complex Structures ◽  
Eeg Signals ◽  
Muscle Activations ◽  
Electroencephalogram Eeg ◽  
The Brain

In this article, we evaluated the variations of the brain and muscle activations while subjects are exposed to different perturbations to walking and standing balance. Since EEG and EMG signals have complex structures, we utilized the complexity-based analysis. Specifically, we analyzed the fractal dimension and sample entropy of Electroencephalogram (EEG) and Electromyogram (EMG) signals while subjects walked and stood, and received different perturbations in the form of pulling and rotation (via virtual reality). The results showed that the complexity of EEG signals was higher in walking than standing as the result of different perturbations. However, the complexity of EMG signals was higher in standing than walking as the result of different perturbations. Therefore, the alterations in the complexity of EEG and EMG signals are inversely correlated. This analysis could be extended to investigate simultaneous variations of rhythmic patterns of other physiological signals while subjects perform different activities.

scholarly journals COMPLEXITY-BASED EVALUATION OF THE CORRELATION BETWEEN HEART AND BRAIN RESPONSES TO MUSIC

Fractals ◽  
2021 ◽  
pp. 2150238
Author(s):  
TISARA KUMARASINGHE ◽  
ONDREJ KREJCAR ◽  
ALI SELAMAT ◽  
NORAZRYANA MAT DAWI ◽  
ENRIQUE HERRERA-VIEDMA ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Sample Entropy ◽  
Auditory Stimuli ◽  
Strong Correlations ◽  
Pink Noise ◽  
Eeg Signals ◽  
Brain Responses ◽  
Brown Noise ◽  
The Difference ◽  
The Brain

The evaluation of the correlation between the activations of various organs has great importance. This work investigated the synchronization of the brain and heart responses to different auditory stimuli using complexity-based analysis. We selected three pieces of music based on the difference in the complexity of embedded noise (including white noise, brown noise, and pink noise) in them. We played these pieces of music for 11 subjects (7 M and 4 F) and computed the fractal dimension and sample entropy of EEG signals and R–R time series [as heart rate variability (HRV)]. We found strong correlations ([Formula: see text] in the case of fractal dimension and [Formula: see text] in the case of sample entropy) among the complexities of EEG signals and HRV. This finding demonstrates the synchronization of the brain and heart responses and auditory stimuli from the complexity perspective.

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.

Information-based decoding of the coupling among human brain activity and movement paths

2021 ◽  
pp. 1-10
Author(s):  
Shahul Mujib Kamal ◽  
Norazryana Mat Dawi ◽  
Hamidreza Namazi
Keyword(s):  
Brain Activity ◽  
Point Of View ◽  
Physiological Signals ◽  
Human Walking ◽  
Eeg Signals ◽  
Rehabilitation Science ◽  
Information Contents ◽  
First Time ◽  
The Brain ◽  
Brain Reaction

BACKGROUND: Walking like many other actions of a human is controlled by the brain through the nervous system. In fact, if a problem occurs in our brain, we cannot walk correctly. Therefore, the analysis of the coupling of brain activity and walking is very important especially in rehabilitation science. The complexity of movement paths is one of the factors that affect human walking. For instance, if we walk on a path that is more complex, our brain activity increases to adjust our movements. OBJECTIVE: This study for the first time analyzed the coupling of walking paths and brain reaction from the information point of view. METHODS: We analyzed the Shannon entropy for electroencephalography (EEG) signals versus the walking paths in order to relate their information contents. RESULTS: According to the results, walking on a path that contains more information causes more information in EEG signals. A strong correlation (p= 0.9999) was observed between the information contents of EEG signals and walking paths. Our method of analysis can also be used to investigate the relation among other physiological signals of a human and walking paths, which has great benefits in rehabilitation science.

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.

Information-based analysis of the coupling between brain and heart reactions to olfactory stimulation

2021 ◽  
pp. 1-11
Author(s):  
Najmeh Pakniyat ◽  
Mohammad Hossein Babini ◽  
Vladimir V. Kulish ◽  
Hamidreza Namazi
Keyword(s):  
Time Series ◽  
Biomedical Science ◽  
Strongly Correlated ◽  
Eeg Signals ◽  
Ecg Signals ◽  
Olfactory Stimuli ◽  
Electrocardiogram Ecg ◽  
First Time ◽  
Electroencephalogram Eeg ◽  
The Brain

BACKGROUND: Analysis of the heart activity is one of the important areas of research in biomedical science and engineering. For this purpose, scientists analyze the activity of the heart in various conditions. Since the brain controls the heart’s activity, a relationship should exist among their activities. OBJECTIVE: In this research, for the first time the coupling between heart and brain activities was analyzed by information-based analysis. METHODS: Considering Shannon entropy as the indicator of the information of a system, we recorded electroencephalogram (EEG) and electrocardiogram (ECG) signals of 13 participants (7 M, 6 F, 18–22 years old) in different external stimulations (using pineapple, banana, vanilla, and lemon flavors as olfactory stimuli) and evaluated how the information of EEG signals and R-R time series (as heart rate variability (HRV)) are linked. RESULTS: The results indicate that the changes in the information of the R-R time series and EEG signals are strongly correlated (ρ=-0.9566). CONCLUSION: We conclude that heart and brain activities are related.

scholarly journals Context-Based Filtering for Assisted Brain-Actuated Wheelchair Driving

2007 ◽  
Vol 2007 ◽  
pp. 1-12 ◽  
Author(s):  
Gerolf Vanacker ◽  
José del R. Millán ◽  
Eileen Lew ◽  
Pierre W. Ferrez ◽  
Ferran Galán Moles ◽  
...  
Keyword(s):  
Control System ◽  
Shared Control ◽  
Eeg Signals ◽  
Intelligent Processing ◽  
Intelligent Wheelchair ◽  
Brain Signals ◽  
Brain Interface ◽  
The Subject ◽  
Electroencephalogram Eeg ◽  
The Brain

Controlling a robotic device by using human brain signals is an interesting and challenging task. The device may be complicated to control and the nonstationary nature of the brain signals provides for a rather unstable input. With the use of intelligent processing algorithms adapted to the task at hand, however, the performance can be increased. This paper introduces a shared control system that helps the subject in driving an intelligent wheelchair with a noninvasive brain interface. The subject's steering intentions are estimated from electroencephalogram (EEG) signals and passed through to the shared control system before being sent to the wheelchair motors. Experimental results show a possibility for significant improvement in the overall driving performance when using the shared control system compared to driving without it. These results have been obtained with 2 healthy subjects during their first day of training with the brain-actuated wheelchair.

scholarly journals Alternative Form of Ordinary Differential Equation of Electroencephalography Signals During an Epileptic Seizure

2021 ◽  
Vol 17 (2) ◽  
pp. 109-113
Author(s):  
Ameen Omar Barja
Keyword(s):  
Differential Equation ◽  
Ordinary Differential Equation ◽  
Epileptic Seizure ◽  
Essential Role ◽  
Clinical Neurophysiology ◽  
Alternative Form ◽  
Eeg Signals ◽  
Seizure Disorders ◽  
Electroencephalogram Eeg ◽  
The Brain

One of the most important fields in clinical neurophysiology is an electroencephalogram (EEG). It is a test used to detect problems related to the brain electrical activity, and it can track and records patterns of brain waves. EEG continues to play an essential role in diagnosis and management of patients with epileptic seizure disorders. Nevertheless, the outcome of EEG as a tool for evaluating epileptic seizure is often interpreted as a noise rather than an ordered pattern. The mathematical modelling of EEG signals provides valuable data to neurologists, and is heavily utilized in the diagnosis and treatment of epilepsy. EEG signals during the seizure can be modeled as ordinary differential equation (ODE). In this study we will present an alternative form of ODE of EEG signals through the seizure.

scholarly journals EVALUATION OF THE CORRELATION BETWEEN FACIAL MUSCLE AND BRAIN ACTIVITIES IN AUDITORY STIMULATION

Fractals ◽  
2021 ◽  
Vol 29 (01) ◽  
pp. 2150100
Author(s):  
MIRRA SOUNDIRARAJAN ◽  
MARTIN AUGUSTYNEK ◽  
ONDREJ KREJCAR ◽  
HAMIDREZA NAMAZI
Keyword(s):  
Human Body ◽  
Strong Correlation ◽  
Rock Music ◽  
Sample Entropy ◽  
Auditory Stimulation ◽  
Auditory Stimuli ◽  
Facial Muscle ◽  
Eeg Signals ◽  
Facial Muscles ◽  
The Brain

Evaluation of the correlation of the activities of various organs is an important area of research in physiology. In this paper, we evaluated the correlation among the brain and facial muscles’ reactions to various auditory stimuli. We played three different music (relaxing, pop, and rock music) to 13 subjects and accordingly analyzed the changes in complexities of EEG and EMG signals by calculating their fractal exponent and sample entropy. Based on the results, EEG and EMG signals experienced more significant changes by presenting relaxing, pop, and rock music, respectively. A strong correlation was observed among the alterations of the complexities of EMG and EEG signals, which indicates the coupling of the activities of facial muscles and brain. This method could be further applied to investigate the coupling of the activities of the brain and other organs of the human body.

scholarly journals Sample Entropy Analysis of EEG Signals via Artificial Neural Networks to Model Patients’ Consciousness Level Based on Anesthesiologists Experience

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
George J. A. Jiang ◽  
Shou-Zen Fan ◽  
Maysam F. Abbod ◽  
Hui-Hsun Huang ◽  
Jheng-Yan Lan ◽  
...  
Keyword(s):  
Sample Entropy ◽  
Expert Assessment ◽  
Ann Model ◽  
Eeg Signals ◽  
Multivariate Empirical Mode Decomposition ◽  
Consciousness Level ◽  
Mode Decomposition ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Electroencephalogram Eeg

Electroencephalogram (EEG) signals, as it can express the human brain’s activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA). Bispectral (BIS) index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD) method and analyzed using sample entropy (SampEn) analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN) model through using expert assessment of consciousness level (EACL) which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully.

scholarly journals Application of Convolutional Neural Network Method in Brain Computer Interface

2021 ◽  
Vol 2078 (1) ◽  
pp. 012044
Author(s):  
Lingzhi Chen ◽  
Wei Deng ◽  
Chunjin Ji
Keyword(s):  
Pattern Recognition ◽  
Recognition Accuracy ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signals ◽  
Network Method ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Bci System

Abstract Pattern Recognition is the most important part of the brain computer interface (BCI) system. More and more profound learning methods were applied in BCI to increase the overall quality of pattern recognition accuracy, especially in the BCI based on Electroencephalogram (EEG) signal. Convolutional Neural Networks (CNN) holds great promises, which has been extensively employed for feature classification in BCI. This paper will review the application of the CNN method in BCI based on various EEG signals.

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