Dynamic Analysis of Human Brain in the Pain State by Electroencephalography

2021 ◽  
Author(s):  
Mahsa Tavasoli ◽  
zahra einalou ◽  
Reza Akhondzadeh
Keyword(s):  
Brain Regions ◽  
Eeg Signal ◽  
Neural Network Classifier ◽  
Unpleasant Sensation ◽  
Quantification Analysis ◽  
Pain State ◽  
Dynamical Features ◽  
Pain Detection ◽  
Electroencephalogram Eeg ◽  
The Brain

Abstract Objective Pain is an unpleasant sensation that is important in all therapeutic conditions. So far, some studies have focused on pain assessment and cognition through different tests and methods. Considering the occurrence of pain causes, along with the activation of a long network in brain regions, recognizing the dynamical changes of the brain in pain states is helpful for pain detection using the electroencephalogram (EEG) signal. Therefore, the present study addressed the above-mentioned issue by applying EEG at the time of inducing phasic pain. Results Phasic pain was produced using coldness and then dynamical features via EEG were analyzed by the Recurrence Quantification Analysis (RQA) method, and finally, the Rough neural network classifier was utilized for achieving accuracy regarding detecting and categorizing pain and non-pain states, which was 95.25\(\pm\)4%. The simulation results confirmed that cerebral behaviors are detectable during pain. In addition, the high accuracy of the classifier for evaluating the dynamical features of the brain during pain occurrence is one of the most merits of the proposed method. Eventually, pain detection can improve medical methods.

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 Electroencephalogram Profiles for Emotion Identification over the Brain Regions Using Spectral, Entropy and Temporal Biomarkers

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 59
Author(s):  
Noor Kamal Al-Qazzaz ◽  
Mohannad K. Sabir ◽  
Sawal Hamid Bin Mohd Ali ◽  
Siti Anom Ahmad ◽  
Karl Grammer
Keyword(s):  
Human Behavior ◽  
Brain Regions ◽  
Entropy Method ◽  
Emotional States ◽  
Spectral Entropy ◽  
Emotion Identification ◽  
Daily Lives ◽  
The University ◽  
Electroencephalogram Eeg ◽  
The Brain

Identifying emotions has become essential for comprehending varied human behavior during our daily lives. The electroencephalogram (EEG) has been adopted for eliciting information in terms of waveform distribution over the scalp. The rationale behind this work is twofold. First, it aims to propose spectral, entropy and temporal biomarkers for emotion identification. Second, it aims to integrate the spectral, entropy and temporal biomarkers as a means of developing spectro-spatial ( S S ) , entropy-spatial ( E S ) and temporo-spatial ( T S ) emotional profiles over the brain regions. The EEGs of 40 healthy volunteer students from the University of Vienna were recorded while they viewed seven brief emotional video clips. Features using spectral analysis, entropy method and temporal feature were computed. Three stages of two-way analysis of variance (ANOVA) were undertaken so as to identify the emotional biomarkers and Pearson’s correlations were employed to determine the optimal explanatory profiles for emotional detection. The results evidence that the combination of applied spectral, entropy and temporal sets of features may provide and convey reliable biomarkers for identifying S S , E S and T S profiles relating to different emotional states over the brain areas. EEG biomarkers and profiles enable more comprehensive insights into various human behavior effects as an intervention on the brain.

scholarly journals Comparison between Daytime and Nighttime Scenery Focusing on Restorative and Recovery Effect

2019 ◽  
Vol 11 (12) ◽  
pp. 3326
Author(s):  
SangHyun Cheon ◽  
Soyoung Han ◽  
Mintai Kim ◽  
Yoonku Kwon
Keyword(s):  
Psychological Impact ◽  
Brain Regions ◽  
Visual Images ◽  
Built Environments ◽  
Brain Response ◽  
Reported Data ◽  
Physiological Variations ◽  
Negative Relationships ◽  
Electroencephalogram Eeg ◽  
The Brain

The overall purpose of this study was to investigate psycho-physiological variations in human bodies by observing visual images of daytime and nighttime scenery to focus on restorative and recovery effects. Unlike previous studies that have focused on the natural versus built environments, this study aims to compare restorative and recovery potentials between daytime and nighttime. The experiment was conducted by showing a total of 12 images to 60 participants in order to measure the brain response with an electroencephalogram (EEG). As measures of the psychological impact of the images, perceived restorative and recovery scales were used. The self-reported data indicates that daytime sceneries are rated more positively than nighttime sceneries in terms of restorative and recovery effects. According to the EEG results, restorative and recovery feelings have negative relationships with the relative theta band, while positive relationships are shown with the relative alpha band. The correlation analysis between EEG bands and brain regions showed a significant correlation (p < 0.05) with 46 pairs for the daytime scenery stimuli and 52 pairs for the nighttime scenery stimuli. Through the results of the study, we conclude that daytime and nighttime scenery affect restorative feelings and the human brain response through both verbal and non-verbal methods.

scholarly journals Analisis Perbedaan Pola Sinyal EEG Berdasarkan Jenis Kelamin Yang Berbeda Saat Numerical Stroop Task

2018 ◽  
Vol 8 (1) ◽  
pp. 107
Author(s):  
Riswandha Latu Dimas ◽  
Catur Atmaji
Keyword(s):  
Stroop Task ◽  
Cognitive Process ◽  
Energy Analysis ◽  
Peak Time ◽  
Eeg Signal ◽  
Observation Process ◽  
Time And Energy ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Numerical Stroop Task

Cognitive process show how brain work from stimulus reception until stimuls reaction. With electroencephalogram (EEG) device, cognate process can be observerd in brain signal or EEG signal form. In cognitive process different kind of stimulus could affect generated brain signal. Also, given interference in cognitive prcess could affect brain signal. In this research, conducted observation whether gender difference has effect in cognitive process. Numerical stroop task with three kinds of conditions (congruence, incongruence, and neutral) are used as reference in signal observation process which is generated when the cognitive process in difference genders are done. The resulting EEG signal then conducted three kinds of analysis that is ERP analysis, reaction time, and energy analysis. The result of ERP analysis show both subject class have difference in response time that indicated with P3 peak time. On average, respons time in female (kongruent = 623,34 ms; inkongruent = 645,18 ms ; neutral = 614,91 ms)subject class is faster than male (kongruent = 709,67 ms; inkongruent = 745,00 ms; neutral =715,37 ms) subject class. Energy analysis show when numerical stroop task takes place, left side of the brain (51,36%) and cetral side of the brain (50,65%) more dominant than others parts of the brain.

scholarly journals Removing Artifacts From EEG Signal Using Wavelet Transform and Conventional Filters

2021 ◽  
Vol 17 ◽  
pp. 177-183
Author(s):  
Meryem Felja ◽  
Asmae Bencheqroune ◽  
Mohammed Karim ◽  
Ghita Bennis Limas
Keyword(s):  
Wavelet Transform ◽  
Eye Movements ◽  
Wavelet Transforms ◽  
Experimental Results ◽  
Eeg Signal ◽  
Electroencephalogram Eeg ◽  
The Brain

the electroencephalogram (EEG) is a signal of an electrical nature reflecting the neuronal activities of the brain. It is used for the diagnosis of certain cerebral pathologies. However, it becomes more difficult to identify and analyze it when it is corrupted by artifacts of non-cerebral origin such as eye movements, cardiac activities ..., therefore, it is essential to remove these parasitic signals. In literature, there are different techniques for removing artifacts. This paper proposes and discusses a new EEG de-noising technique, based on a combination of wavelet transforms and conventional filters. The experimental results demonstrate that the proposed approach can be an effective tool for removing artifact without suppression of any signal components.

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.

scholarly journals The analysis of the influence of fractal structure of stimuli on fractal dynamics in fixational eye movements and EEG signal

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Hamidreza Namazi ◽  
Vladimir V. Kulish ◽  
Amin Akrami
Keyword(s):  
Eye Movements ◽  
Visual Stimulus ◽  
Fractal Structure ◽  
Temporal Structure ◽  
Human Vision ◽  
Eeg Signal ◽  
Vision Disorders ◽  
Fixational Eye Movements ◽  
Electroencephalogram Eeg ◽  
The Brain

Abstract One of the major challenges in vision research is to analyze the effect of visual stimuli on human vision. However, no relationship has been yet discovered between the structure of the visual stimulus and the structure of fixational eye movements. This study reveals the plasticity of human fixational eye movements in relation to the ‘complex’ visual stimulus. We demonstrated that the fractal temporal structure of visual dynamics shifts towards the fractal dynamics of the visual stimulus (image). The results showed that images with higher complexity (higher fractality) cause fixational eye movements with lower fractality. Considering the brain, as the main part of nervous system that is engaged in eye movements, we analyzed the governed Electroencephalogram (EEG) signal during fixation. We have found out that there is a coupling between fractality of image, EEG and fixational eye movements. The capability observed in this research can be further investigated and applied for treatment of different vision disorders.

scholarly journals EEG Signal Research for Identification of Epilepsy using Machine Learning Classification Accession

2019 ◽  
Vol 8 (6S4) ◽  
pp. 683-688
Keyword(s):  
Feature Extraction ◽  
Cell Activity ◽  
Vital Role ◽  
Brain Cell ◽  
Svm Classifier ◽  
Eeg Signal ◽  
Machine Learning Classification ◽  
Mode Decomposition ◽  
Electroencephalogram Eeg ◽  
The Brain

Epilepsy is censorious neurological disorder in which nerve cell activity in the brain is disturbed causing recurrent seizures which are sudden, uncontrolled electrical discharges in the brain cell. In clinical treatment of epileptic patients seizure reorganization has much prominence. Hence in detecting the phenomenon of epilepsy Electroencephalogram (EEG) signal is widely used as it includes important carnal data of the brain. Though it is critical to analyze the EEG signal and identify the seizures. So feature extraction of EEG signal plays a vital role for epilepsy detection. This paper describes an worthwhile feature extraction based on variational mode decomposition (VMD) to identify epilepsy. The extracted features fed to ANN, KNN and SVM in order to classify epilepsy. The performance of the SVM classifier shows the better classification compared to existing methods.

scholarly journals Application of Deep Learning for Emotion Recognition

YMER Digital ◽  
2021 ◽  
Vol 20 (12) ◽  
pp. 834-840
Author(s):  
Varsha R Toshniwal ◽  
◽  
Pooja S Puri ◽  
Keyword(s):  
Network Architecture ◽  
Emotional State ◽  
Human Perception ◽  
Brain Regions ◽  
Eeg Signals ◽  
Neural Network Architecture ◽  
Computer Interfaces ◽  
Alpha Beta ◽  
Electroencephalogram Eeg ◽  
The Brain

The electroencephalogram (EEG) gained a lot of importance in recent years because of its property to depict the nature and actions of human perception. EEG signals are good at capturing the emotional state of a person by measuring the neuronal activities in different regions of the brain. Lots of EEG-based brain-computer interfaces with a different number of channels ( 62 channels, 32 channels, etc.) are being used to capture neuronal activities which can be segmented into different frequency ranges (delta, theta, alpha. beta and gamma). This paper puts forward a neural network architecture for the recognition of emotion from EEG signals and a study providing the set of brain regions and the frequency type associated with the corresponding brain region which contributes most for the detection of emotion though EEG signals. For experimentation, SEED-IV dataset has been used

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