Investigating the Brain Development in Newborns by Information-Based Analysis of Electroencephalography (EEG) Signal

2020 ◽  
Vol 19 (04) ◽  
pp. 2050043 ◽  
Author(s):  
Hamidreza Namazi
Keyword(s):  
Information Theory ◽  
Statistical Analysis ◽  
Brain Development ◽  
Information Content ◽  
Shannon Entropy ◽  
Eeg Signal ◽  
Eeg Signals ◽  
The Brain

In this paper, we employ the information theory to analyze the development of brain as the newborn ages. We compute the Shannon entropy of Electroencephalography (EEG) signal during sleep for 10 groups of newborns who are aged 36 weeks to 45 weeks (first to the last group). Based on the obtained results, EEG signals for newborns in 36 weeks have the lowest information content, whereas EEG signals for newborns in 45 weeks show the greatest information content. Therefore, we concluded that the information content of EEG signal increases as the age of newborn increases. Th result of statistical analysis demonstrated that the influence of increment of age of newborn on the variations of informant content of their EEG signals was significant.

Information-Based Classification of Electroencephalography (EEG) Signals for Healthy Adolescents and Adolescents with Symptoms of Schizophrenia

2020 ◽  
Vol 19 (04) ◽  
pp. 2050033 ◽  
Author(s):  
Hamidreza Namazi
Keyword(s):  
Information Content ◽  
Shannon Entropy ◽  
Healthy Subjects ◽  
Brain Activity ◽  
Research Area ◽  
Eeg Signal ◽  
Brain Disorders ◽  
Eeg Signals ◽  
First Time ◽  
The Brain

Analysis of the brain activity is the major research area in human neuroscience. Besides many works that have been conducted on analysis of brain activity in case of healthy subjects, investigation of brain activity in case of patients with different brain disorders also has aroused the attention of many researchers. An interesting category of works belong to the comparison of brain activity between healthy subjects and patients with brain disorders. In this research, for the first time, we compare the brain activity between adolescents with symptoms of schizophrenia and healthy subjects, by information-based analysis of their Electroencephalography (EEG) signals. For this purpose, we benefit from the Shannon entropy as the indicator of information content. Based on the results of analysis, EEG signal in case of healthy subjects contains more information than EEG signal in case of subjects with schizophrenia. The result of statistical analysis showed the significant variation in the Shannon entropy of EEG signal between healthy adolescents and adolescents with symptoms of schizophrenia in case of P3, O1 and O2 channels. The employed method of analysis in this research can be further extended in order to investigate the variations in the information content of EEG signal in case of subjects with other brain disorders versus healthy subjects.

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.

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.

scholarly journals Nonlinear analysis of scalp EEGs from normal and brain tumour subjects

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Salai Selvam V ◽  
Shenbaga Devi S
Keyword(s):  
Brain Tumour ◽  
Nonlinear Dynamic ◽  
Normal Subjects ◽  
Eeg Signal ◽  
Dynamic Features ◽  
Eeg Signals ◽  
Scalp Eeg ◽  
Pathological Conditions ◽  
Tumour Group ◽  
The Brain

AbstractMeasurement of features from the chaos theory or as popularly known, the concept of nonlinear dynamics, as indicatives of several pathological conditions and cognition states using the electroencephalography (EEG) signal is very popular. In this paper, the analysis of scalp EEG signals of normal subjects and brain tumour patients using the nonlinear dynamic features has been presented. The nonlinear dynamic features that represent the dimensional and waveform complexities of the signal being analyzed have been considered. The statistical analysis of the selected nonlinear dynamic features has been presented. The results show that the nonlinear dynamic features significantly discriminate the brain tumour group from the normal group.

scholarly journals Methods for the Analysis of EEG signals: A Review

2021 ◽  
Vol 9 (9) ◽  
pp. 873-876
Author(s):  
Malika Garg
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Electrical Activity ◽  
Signal Analysis ◽  
Eeg Signal ◽  
Eeg Analysis ◽  
Eeg Signals ◽  
Processing Techniques ◽  
The Brain ◽  
Eeg Signal Analysis

Abstract: Electroencephalography (EEG) helps to predict the state of the brain. It tells about the electrical activity going on in the brain. Difference of the surface potential evolved from various activities get recorded as EEG. The analysis of these EEG signals is of utmost importance to solve the problems related to the brain. Signal pre-processing, feature extraction and classification are the main steps of the EEG signal analysis. In this article we discussed various processing techniques of EEG signals. Keywords: EEG, analysis, signal processing, feature extraction, classification

scholarly journals Best Feature Extraction and Classification Algorithms for EEG Signals in Neuromarketing

2020 ◽  
Author(s):  
Jafar Zamani ◽  
Ali Boniadi Naieni
Keyword(s):  
Feature Extraction ◽  
Brain Activity ◽  
Support Vector ◽  
High Temporal Resolution ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Eeg Signals ◽  
High Efficient ◽  
The Brain

Purpose: There are many methods for advertisements of products and neuromarketing is new area in this field. In neuromarketing, we use neuroscience information for revealing Consumer behavior by extracting brain activity. Functional Magnetic Resonance Imaging (fMRI), Magnetoencephalography (MEG), and Electroencephalography (EEG) are high efficient tools for investigating the brain activity in neuromarketing. EEG signal is a high temporal resolution and a cheap method for examining the brain activity. Materials and Methods: 32 subjects (16 males and 16 females) aging between 20-35 years old participated in this study. We proposed neuromarketing method exploit EEG system for predicting consumer preferences while they view E-commerce products. We apply some important preprocessing steps for noise and artifacts elimination of the EEG signal. In next step feature extraction methods are applied on the EEG data such as Discrete Wavelet Transform (DWT) and statistical features. The goal of this study is classification of analyzed EEG signal to likes and dislikes using supervised algorithms. We use Support Vector Machine (SVM), Artificial Neural Network (ANN) and Random Forest (RF) for data classification. The mentioned methods were used for whole and lobe brain data. Results: The results show high efficacy for SVM algorithms than other methods. Accuracy, sensitivity, specificity and precision parameters were used for evaluation of the model performance. The results show high performance of SVM algorithms for classification of the data with accuracy more than 87% and 84% for whole and parietal lobe data. Conclusion: We designed a tool with EEG signals for extraction brain activity of consumers using neuromarketing methods. We investigated the effects of advertising on brain activity of consumers by EEG signals measures.

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 Eeg signal classification for epileptic seizure using MATLAB

2019 ◽  
Vol 8 (9) ◽  
pp. 1458-1461
Keyword(s):  
Epileptic Seizure ◽  
Neurological Disorder ◽  
Matrix Algebra ◽  
General Purpose ◽  
Signal Classification ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Computer Aided ◽  
Eeg Signal Classification ◽  
The Brain

Epilepsy is explained as a group of neurological disorder which may be characterized by epileptic seizure within some consequence of unconstraints because of peculiar cortical nerve cell activities in the brain. The manual detection of this disorder by a neurologist is expensive and time consuming also and there may be some loss of accuracy because of fatigue, computer aided approach etc. This work has proposed a method which is highly efficient and gives accurate results over electroencephalogram signals for epilepsy. MATLAB is a very popular, powerful, general-purpose system or it can be said that it is an general purpose environment for matrix algebra calculations and many other more specific computations. It has various applications in Aerospace, Biology, Finance, data acquisition, etc. Here Matlab is used to classify the EEG signals over some parameters. Here a program is developed in MATLAB to check the condition of signal whether it is healthy or unhealthy

DETECTION OF THE ONSET OF EPILEPTIC SEIZURE SIGNAL FROM SCALP EEG USING BLIND SIGNAL SEPARATION

2009 ◽  
Vol 21 (04) ◽  
pp. 287-290 ◽  
Author(s):  
M. Moghavvemi ◽  
S. Mehrkanoon
Keyword(s):  
Signal Separation ◽  
Blind Signal Separation ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Blind Signal ◽  
Scalp Eeg ◽  
Mapping Process ◽  
Independent Features ◽  
Electroencephalogram Eeg ◽  
The Brain

Investigation of epileptic electroencephalogram (EEG) signal is one of the major areas of study in the field of signal processing. The ability to detect the seizure signal and its origin within the brain is of prime importance. This paper proposes a sequential blind signal separation (BSS) based system to extract the seizure signal from scalp EEG and to pinpoint the main location of seizure signal within the brain. BSS algorithm is used to demix the EEG signal into signals with independent features. Scalp time-mapping process is applied to determine the main location of the extracted seizure signal within the brain. The algorithm has been tested on epileptic EEG signals recorded from patients for detection of the onset of seizure waves and their origin within the brain.

scholarly journals COMPLEXITY AND INFORMATION-BASED ANALYSIS OF THE ELECTROENCEPHALOGRAM (EEG) SIGNALS IN STANDING, WALKING, AND WALKING WITH A BRAIN–COMPUTER INTERFACE

Fractals ◽  
2021 ◽  
Author(s):  
JANARTHANAN RAMADOSS ◽  
NORAZRYANA MAT DAWI ◽  
KARTHIKEYAN RAJAGOPAL ◽  
HAMIDREZA NAMAZI
Keyword(s):  
Information Content ◽  
Shannon Entropy ◽  
Brain Computer Interface ◽  
Fractal Theory ◽  
Brain Activation ◽  
Computer Interface ◽  
Complex Structures ◽  
Eeg Signals ◽  
Electroencephalogram Eeg

In this paper, we analyzed the variations in brain activation between different activities. Since Electroencephalogram (EEG) signals as an indicator of brain activation contain information and have complex structures, we employed complexity and information-based analysis. Specifically, we used fractal theory and Shannon entropy for our analysis. Eight subjects performed three different activities (standing, walking, and walking with a brain–computer interface) while their EEG signals were recorded. Based on the results, the complexity and information content of EEG signals have the greatest and smallest values in walking and standing, respectively. Complexity and information-based analysis can be applied to analyze the activations of other organs in different conditions.

Sign in / Sign up

Export Citation Format

Share Document