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 Analysis of the Influence of Complexity and Entropy of Odorant on Fractal Dynamics and Entropy of EEG Signal

2016 ◽  
Vol 2016 ◽  
pp. 1-5 ◽  
Author(s):  
Hamidreza Namazi ◽  
Amin Akrami ◽  
Sina Nazeri ◽  
Vladimir V. Kulish
Keyword(s):  
Brain Research ◽  
Approximate Entropy ◽  
Olfactory Stimulus ◽  
Brain Diseases ◽  
Eeg Signal ◽  
Olfactory Stimuli ◽  
Important Challenge ◽  
Molecular Complexity ◽  
Electroencephalogram Eeg

An important challenge in brain research is to make out the relation between the features of olfactory stimuli and the electroencephalogram (EEG) signal. Yet, no one has discovered any relation between the structures of olfactory stimuli and the EEG signal. This study investigates the relation between the structures of EEG signal and the olfactory stimulus (odorant). We show that the complexity of the EEG signal is coupled with the molecular complexity of the odorant, where more structurally complex odorant causes less fractal EEG signal. Also, odorant having higher entropy causes the EEG signal to have lower approximate entropy. The method discussed here can be applied and investigated in case of patients with brain diseases as the rehabilitation purpose.

scholarly journals Signal Dynamics Analysis for Epileptic Seizure Classification on EEG Signals

2021 ◽  
Vol 38 (1) ◽  
pp. 73-78
Author(s):  
Sugondo Hadiyoso ◽  
Inung Wijayanto ◽  
Annisa Humairani
Keyword(s):  
Processing System ◽  
Detection Accuracy ◽  
Eeg Signal ◽  
Test Results ◽  
Eeg Signals ◽  
Signal Processing System ◽  
Patients With Epilepsy ◽  
Electroencephalogram Eeg ◽  
Seizure Classification

Epilepsy is the most common form of neurological disease. Patients with epilepsy may experience seizures of a certain duration with or without provocation. Epilepsy analysis can be done with an electroencephalogram (EEG) examination. Observation of qualitative EEG signals generates high cost and often confuses due to the nature of the non-linear EEG signal and noise. In this study, we proposed an EEG signal processing system for EEG seizure detection. The signal dynamics approach to normal and seizure signals' characterization became the main focus of this study. Spectral Entropy (SpecEn) and fractal analysis are used to estimate the EEG signal dynamics and used as feature sets. The proposed method is validated using a public EEG dataset, which included preictal, ictal, and interictal stages using the Naïve Bayes classifier. The test results showed that the proposed method is able to generate an ictal detection accuracy of up to 100%. It is hoped that the proposed method can be considered in the detection of seizure signals on the long-term EEG recording. Thus it can simplify the diagnosis of epilepsy.

Magnetoencephalography

2016 ◽  
Author(s):  
Paul L. Furlong ◽  
Elaine Foley ◽  
Caroline Witton ◽  
Stefano Seri
Keyword(s):  
Age Groups ◽  
Seizure Freedom ◽  
Additional Information ◽  
Epileptogenic Lesion ◽  
Recent Developments ◽  
Paediatric Age ◽  
Important Additional Information ◽  
Movement Compensation ◽  
Patients With Epilepsy ◽  
Electroencephalogram Eeg

For presurgical assessments for resection of an epileptogenic lesion or zone, evaluations over the last 20 years have established magnetoencephalography (MEG) as a valuable tool in routine clinical practice in both adult and paediatric age groups. MEG can accurately localize both ictal and inter-ictal spike sources. MEG yields important additional information in around 30% of patients with epilepsy of suspected neocortical origin, aiding in the modification or extension of invasive measurements. Seizure freedom is most likely to occur when there is concordance between electroencephalogram (EEG) and MEG localization, and least likely to occur when these results are divergent. In some patients, invasive recordings may not be viable or repeatable. In these cases, MEG localization frequently provides additional information for planning surgery. Recent developments in technology for movement compensation and enhanced noise reduction provide optimism for continually improving outcomes of MEG-enhanced presurgical evaluations.

P300 Feature Extraction of Visual and Auditory Evoked EEG Signal

2014 ◽  
Vol 490-491 ◽  
pp. 1374-1377 ◽  
Author(s):  
Xiao Yan Qiao ◽  
Jia Hui Peng
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Evoked Potentials ◽  
Brain Computer Interface ◽  
Auditory Stimulation ◽  
Computer Interface ◽  
Eeg Signal ◽  
Significant Issue ◽  
Eeg Signals ◽  
Electroencephalogram Eeg

It is a significant issue to accurately and quickly extract brain evoked potentials under strong noise in the research of brain-computer interface technology. Considering the non-stationary and nonlinearity of the electroencephalogram (EEG) signal, the method of wavelet transform is adopted to extract P300 feature from visual, auditory and visual-auditory evoked EEG signal. Firstly, the imperative pretreatment to EEG acquisition signals was performed. Secondly, respectivly obtained approximate and detail coefficients of each layer, by decomposing the pretreated signals for five layers using wavelet transform. Finally, the approximate coefficients of the fifth layer were reconstructed to extract P300 feature. The results have shown that the method can effectively extract the P300 feature under the different visual-auditory stimulation modes and lay a foundation for processing visual-auditory evoked EEG signals under the different mental tasks.

scholarly journals EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed TarekShahriar Abstract—An EMD-chaos based approach is proposed todiscriminate EEG signals corresponding to healthy persons,and epileptic patients during seizure-free intervals and seizureattacks. An electroencephalogram (EEG) is first empiricallydecomposed to intrinsic mode functions (IMFs). The nonlineardynamics of these IMFs are quantified in terms of the largestLyapunov exponent (LLE) and correlation dimension (CD).This chaotic analysis in EMD domain is applied to a large groupof EEG signals corresponding to healthy persons as well asepileptic patients (both with and without seizure attacks). It isshown that the values of the obtained LLE and CD exhibitfeatures by which EEG for seizure attacks can be clearlydistinguished from other EEG signals in the EMD domain.Thus, the proposed approach may aid researchers in developingeffective techniques to predict seizure activities. Index Terms—Electroencephalogram (EEG), empiricalmode decomposition (EMD), largest Lyapunov exponent (LLE),correlation dimension (CD), epileptic seizures. The Authors are with the Electrical and Electronic EngineeringDepartment, Bangladesh University of Engineering and Technology,Dhaka-1000, Bangladesh (e-mail: [email protected]) [PDF] Cite: S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed Tarek Shahriar, "EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain," International Journal of Computer and Electrical Engineering vol. 4, no. 3, pp. 326-330, 2012. PREVIOUS PAPER Perception of Emotions Using Constructive Learningthrough Speech NEXT PAPER Physical Layer Impairments Aware OVPN Connection Selection Mechanisms Copyright © 2008-2013. International Association of Computer Science and Information Technology Press (IACSIT Press)

2012 ◽  
pp. 326-330 ◽  
Author(s):  
S. M. Shafiul Alam ◽  
S. M. Shafiul Alam ◽  
Aurangozeb ◽  
Syed TarekShahriar
Keyword(s):  
Correlation Dimension ◽  
Largest Lyapunov Exponent ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Linear Dynamics ◽  
Signal Discrimination ◽  
Non Linear ◽  
Electroencephalogram Eeg ◽  
Seizure Attacks ◽  
Healthy Persons

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

Fractals ◽  
2019 ◽  
Vol 27 (03) ◽  
pp. 1950041 ◽  
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.

scholarly journals Denoising of EEG Signal using Matlab and SIMULINK Techniques and Estimation of Power Spectral Density of EEG Signal using SIMULINK AR Models

2019 ◽  
Vol 9 (2) ◽  
pp. 418-422
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Signal To Noise Ratio ◽  
Standard Technique ◽  
Research Paper ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Power Spectral ◽  
Electroencephalogram Eeg ◽  
Ocular Artifacts

The Electroencephalogram (EEG) is the standard technique for investigating the brain’s electrical activity in different psychological and pathological states. Analysis of Electroencephalogram (EEG) signal is a challenging task by reason of the presence of different artifacts such as Ocular Artifacts (OA) and Electromyogram. Normally EEG signals falls in the frequency range of DC to 60 Hz and amplitude of 1-5 µv. Ocular artifacts do have the similar statistical properties of EEG signals, often interfere with EEG signal, thereby making the analysis of EEG signals more complex. In this research paper, removal of artifacts was done using wavelets (matlab coding) as well as using SIMULINK DWT and IDWT blocks and estimated the SNR. In the next stage the output of IDWT block was taken as input to Burg model and Yule walker model to estimate the power spectral density of EEG signal by setting the various parameters of the blocks. The implementation of denoising of EEG signal using SIMULINK DWT and IDWT blocks and estimation of power spectral density of denoised EEG signal using Burg model and Yule walker model was explained in detail in the paper under the methodology heading. In this research paper, the collected EEG signal is normalized and later linearly mixed with the normalized EOG signal resulting in a noisy EEG signal. This noisy EEG signal is decomposed to 4 levels by using different wavelets. This decomposition of EEG signals yields approximate and detail coefficients. Later different thresholding techniques were applied to detail coefficients and estimated the Signal to Noise Ratio of it and estimated the power spectral density of denoised EEG signal obtained from dB4 wavelet as it is providing better SNR than other wavelets mentioned in the results.

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 Development of an EEG signal analysis application through a convolution of a complex Morlet wavelet: preliminary results

2019 ◽  
Vol 8 (2) ◽  
pp. B1-1-B1-19
Author(s):  
José Humberto Trueba Perdomo ◽  
◽  
Ignacio Herrera Aguilar ◽  
Francesca Gasparini ◽  
◽  
...  
Keyword(s):  
Morlet Wavelet ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Eyes Closed ◽  
Eyes Open ◽  
Analysis Application ◽  
The Subject ◽  
Complex Morlet Wavelet ◽  
Electroencephalogram Eeg ◽  
Eeg Signal Analysis

This paper presents a new application for analyzing electroencephalogram (EEG) signals. The signals are pre-filtered through MATLAB's EEGLAB tool. The created application performs a convolution between the original EEG signal and a complex Morlet wavelet. As a final result, the application shows the signal power value and a spectrogram of the convoluted signal. Moreover, the created application compares different EEG channels at the same time, in a fast and straightforward way, through a time and frequency analysis. Finally, the effectiveness of the created application was demonstrated by performing an analysis of the alpha signals of healthy subjects, one signal created by the subject with eyes closed and the other, with which it was compared, was created by the same subject with eyes open. This also served to demonstrate that the power of the alpha band of the closed-eyed signal is higher than the power of the open-eyed signal.

scholarly journals De-noising of EEG Signals With Shift Based Cycle Spinning on Wave Atoms

2021 ◽  
Author(s):  
M. Purnachandra Rao ◽  
E. Srinivasa R
Keyword(s):  
Signal Analysis ◽  
Eeg Signal ◽  
Brain Disorders ◽  
Performance Measurements ◽  
Eeg Signals ◽  
Efficient Manner ◽  
Wave Atoms ◽  
Cycle Spinning ◽  
Wave Atom Transform ◽  
Better Than

Abstract To predict our brain disorders, EEG signals need to be analyzed. However, most EEG signals were affected by different kinds of noise during their acquision. So, signal analysis become most difficult due to the contamination of various noise. An appropriate technique is necessary to remove the noise from the signal. Wavelet Transform is one the most widely used technique for removing the noise from EEG signals. Wave atom is one of the new multiscale-multidirectional transforms, which is better than both wavelet as well as curvelet transforms. This wave atom transform has good orientation characteristic by which it preserves the edges in an efficient manner. This paper introduced a new method for denoising of EEG signal by shift-based cycle spinning on wave atom transform. Cycle spinning is a technique can be used to enhance the capability of wave atoms. An original EEG signals from public EEG database were used for this experiment. The results are analysed based on the performance measurements like SNR and MSE. The experimental results show that cycle spinning technique with appropriate shifts could be the better choice to denoise an EEG signals.

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