scholarly journals Epilepsy Identification using EEG signal monitoring

2021 ◽  
Vol 12 (2) ◽  
pp. 2366-2371
Author(s):  
Ganesh Birajadar, Et. al.
Keyword(s):  
Behavior Analysis ◽  
Frequency Domain ◽  
Human Behavior ◽  
Electric Activity ◽  
Eeg Signal ◽  
Brain Abnormalities ◽  
Eeg Signals ◽  
Signal Monitoring ◽  
Non Stationary Signal ◽  
Electroencephalogram Eeg

Electroencephalogram (EEG) is nothing but measuring electric activity of brain. EEG is non-stationary signal. EEG characterizes human behavior. There are many brain abnormalities that can be identified and treated using EEG behavior analysis. As per researchers study Epilepsy is commonly happening disorder that is getting spread over the time. It is nothing but sudden stroke in brain where patient suffers from unusual activities seizures. Sometimes symptoms are such severe that ignorance leads to death. So it is important to identify its earlier symptoms and treat it in time so as to avoid risk. EEG signals are used for getting features in time as well as frequency domain. These features are further analyzed and classified to identify EEG abnormality.  

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.

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

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 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.

DECODING OF UPPER LIMB MOVEMENT BY FRACTAL ANALYSIS OF ELECTROENCEPHALOGRAM (EEG) SIGNAL

Fractals ◽  
2018 ◽  
Vol 26 (05) ◽  
pp. 1850081 ◽  
Author(s):  
HAMIDREZA NAMAZI ◽  
TIRDAD SEIFI ALA ◽  
VLADIMIR KULISH
Keyword(s):  
Fractal Analysis ◽  
Fractal Theory ◽  
Elbow Flexion ◽  
Eeg Signal ◽  
Limb Movements ◽  
Eeg Signals ◽  
Human Movements ◽  
Rest Condition ◽  
First Time ◽  
Electroencephalogram Eeg

Analysis of human movements is an important category of research in biomedical engineering, especially for the rehabilitation purpose. The movement of limbs is investigated usually by analyzing the movement signals. Less efforts have been made to investigate how neural that correlate to the movements, are represented in the human brain. In this research, for the first time we decode the limb movements by fractal analysis of Electroencephalogram (EEG) signals. We investigated how the complexity of EEG signal changes in different limb movements in motor execution (ME), and motor imagination (MI) sessions. The result of our analysis showed that the EEG signal experiences greatest level of complexity in elbow flexion and hand-close movements in ME, and MI sessions respectively. On the other hand, the lowest level of complexity of EEG signal belongs to hand-open and rest condition in ME, and MI sessions, respectively. Employing fractal theory in analysis of bio signals is not limited to EEG signal, and can be further investigated in other types of human’s bio signals in different conditions. The result of these investigations can vastly been employed for the rehabilitation purpose.

scholarly journals Automated elimination of EOG artifacts in sleep EEG using regression method

2019 ◽  
Author(s):  
MEHMET DURSUN ◽  
SERAL ÖZŞEN ◽  
SALİH GÜNEŞ ◽  
BAYRAM AKDEMİR ◽  
ŞEBNEM YOSUNKAYA
Keyword(s):  
Clinical Utility ◽  
Regression Method ◽  
Sleep Eeg ◽  
Eeg Signal ◽  
Sleep Staging ◽  
Eeg Signals ◽  
Clinical Tool ◽  
Important Improvement ◽  
Electroencephalogram Eeg ◽  
Signal Sources

Sleep electroencephalogram (EEG) signal is an important clinical tool for automatic sleep staging process. Sleep EEG signal is effected by artifacts and other biological signal sources, such as electrooculogram (EOG) and electromyogram (EMG), and since it is effected, its clinical utility reduces. Therefore, eliminating EOG artifacts from sleep EEG signal is a major challenge for automatic sleep staging. We have studied the effects of EOG signals on sleep EEG and tried to remove them from the EEG signals by using regression method. The EEG and EOG recordings of seven subjects were obtained from the Sleep Research Laboratory of Meram Medicine Faculty of Necmettin Erbakan University. A dataset consisting of 58 h and 6941 epochs was used in the research. Then, in order to see the consequences of this process, we classified pure sleep EEG and artifact-eliminated EEG signals with artificial neural networks (ANN). The results showed that elimination of EOG artifacts raised the classification accuracy on each subject at a range of 1%– 1.5%. However, this increase was obtained for a single parameter. This can be regarded as an important improvement if the whole system is considered. However, different artifact elimination strategies combined with different classification methods for another sleep EEG artifact may give higher accuracy differences between original and purified signals.

scholarly journals Classification of Motor Functions from Electroencephalogram (EEG) Signals Based on an Integrated Method Comprised of Common Spatial Pattern and Wavelet Transform Framework

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4878 ◽  
Author(s):  
Norashikin Yahya ◽  
Huwaida Musa ◽  
Zhong Yi Ong ◽  
Irraivan Elamvazuthi
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet ◽  
Eeg Signal ◽  
Parietal Region ◽  
Eeg Signals ◽  
Common Spatial Pattern ◽  
Band Pass ◽  
Rgb Images ◽  
Electroencephalogram Eeg

In this work, an algorithm for the classification of six motor functions from an electroencephalogram (EEG) signal that combines a common spatial pattern (CSP) filter and a continuous wavelet transform (CWT), is investigated. The EEG data comprise six grasp-and-lift events, which are used to investigate the potential of using EEG as input signals with brain computer interface devices for controlling prosthetic devices for upper limb movement. Selected EEG channels are the ones located over the motor cortex, C3, Cz and C4, as well as at the parietal region, P3, Pz and P4. In general, the proposed algorithm includes three main stages, band pass filtering, CSP filtering, and wavelet transform and training on GoogLeNet for feature extraction, feature learning and classification. The band pass filtering is performed to select the EEG signal in the band of 7 Hz to 30 Hz while eliminating artifacts related to eye blink, heartbeat and muscle movement. The CSP filtering is applied on two-class EEG signals that will result in maximizing the power difference between the two-class dataset. Since CSP is mathematically developed for two-class events, the extension to the multiclass paradigm is achieved by using the approach of one class versus all other classes. Subsequently, continuous wavelet transform is used to convert the band pass and CSP filtered signals from selected electrodes to scalograms which are then converted to images in grayscale format. The three scalograms from the motor cortex regions and the parietal region are then combined to form two sets of RGB images. Next, these RGB images become the input to GoogLeNet for classification of the motor EEG signals. The performance of the proposed classification algorithm is evaluated in terms of precision, sensitivity, specificity, accuracy with average values of 94.8%, 93.5%, 94.7%, 94.1%, respectively, and average area under the receiver operating characteristic (ROC) curve equal to 0.985. These results indicate a good performance of the proposed algorithm in classifying grasp-and-lift events from EEG signals.

scholarly journals Brain connectivity at different time-scales measured with EEG

2005 ◽  
Vol 360 (1457) ◽  
pp. 1015-1024 ◽  
Author(s):  
T Koenig ◽  
D Studer ◽  
D Hubl ◽  
L Melie ◽  
W.K Strik
Keyword(s):  
Frequency Domain ◽  
Brain Connectivity ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Brain Regions ◽  
Frequency Domain Analysis ◽  
Functional Magnetic Resonance ◽  
Eeg Signals ◽  
Electroencephalogram Eeg ◽  
The Brain

We present an overview of different methods for decomposing a multichannel spontaneous electroencephalogram (EEG) into sets of temporal patterns and topographic distributions. All of the methods presented here consider the scalp electric field as the basic analysis entity in space. In time, the resolution of the methods is between milliseconds (time-domain analysis), subseconds (time- and frequency-domain analysis) and seconds (frequency-domain analysis). For any of these methods, we show that large parts of the data can be explained by a small number of topographic distributions. Physically, this implies that the brain regions that generated one of those topographies must have been active with a common phase. If several brain regions are producing EEG signals at the same time and frequency, they have a strong tendency to do this in a synchronized mode. This view is illustrated by several examples (including combined EEG and functional magnetic resonance imaging (fMRI)) and a selective review of the literature. The findings are discussed in terms of short-lasting binding between different brain regions through synchronized oscillations, which could constitute a mechanism to form transient, functional neurocognitive networks.

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.

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