جستجوی مقالات مرتبط با کلیدواژه « الکتروانسفالوگرافی » در نشریات گروه « پزشکی »

Migraine disease is the second most common cause of headaches. Despite the high prevalence, the exact etiology of migraine is yet unknown. In this study, to evaluate the behavior change of electroencephalography (EEG) signals in migraine patients, various features of the EEG signals of migraine patients and healthy controls (HCs) were extracted and compared.

This cross-sectional analytical study was conducted on 21 HCs and 18 migraine patients. Various features, such as fractal dimension (FD), approximate entropy (ApEn), and largest Lyapunov exponent (LLE), were calculated from the EEG signals of migraine patients and HCs. Then different frequency sub-bands of delta, theta, alpha, beta, and gamma were extracted using the wavelet transform, and the energy of these sub-bands was computed. By calculating the mean and variance of the features and applying statistical tests, the feature changes were compared between two groups, and channels with significant differences were identified.

The mean of ApEn, FD and energy of all frequency sub-bands in most of the analyzed channels was higher in migraine patients than in HCs. The mean LLE was mostly lower in migraine patients than in healthy controls. According to the statistical tests, the energy of theta and delta frequency sub-bands with 36 and 35 channels was the feature with the highest number of channels, with a significant difference. In this study, P values less than 0.05 were considered statistically significant.

Migraine patients may have a less sophisticated brain dynamic system due to an increase in irregularity and randomness, as indicated by an increase in ApEn and a decrease in FD in their EEG signals compared to HCs. Anxiety, tension, and other intense sentiments and emotions, as well as the creation of new neural circuits in the brain, can all contribute to an overall increase in energy across all frequency sub-bands in migraine patients.

Contamination Obsessive-Compulsive Disorder (C-OCD) is one of the most common subtypes of OCD. Recently, transcranial direct current stimulation (tDCS) has been suggested as a new solution for improving symptoms in patients with OCD. Evaluating the effectiveness of tDCS through electroencephalogram (EEG) signals can provide a better estimate of improvement and reveal how tDCS leads to changes in the dynamics and features of brain signals. Selecting the optimal features of EEG signals among different features is necessary to show the impact of tDCS. Hence, this study aimed to identify features that undergo substantial changes following tDCS intervention.

10 patients with C-OCD received 20 minutes of tDCS in 10 sessions. The cathode electrode was placed on the left orbitofrontal cortex, and the anode on the cerebellar area. Before and after receiving tDCS, the Yale-Brown Obsession scale (Y-BOCS) was completed, and EEG signals were recorded at rest with open and closed eyes. Then, features such as Fuzzy Synchronization Likelihood (FSL), power spectrum, and Recurrence Quantification Analysis (RQA) were extracted from the EEG signals. Then, the Relief algorithm selected optimal features based on tDCS effectiveness.

The Relief algorithm revealed that RQA indices were more optimal for reflecting tDCS impact compared to other features among those extracted from EEG signals. Moreover, the DET and Lmax values significantly increased after tDCS intervention.

By influencing neural interactions and balancing neuronal activity, tDCS has caused changes in the brain complexity of patients with C-OCD. As a result, there is a correlation between the effectiveness estimated by the Y-BOCS and the features selected by the relief algorithm. tDCS alters brain complexity in EEG compared with other features in C-OCD patients.

Prostration is the best position to discharge electrostatic fields from the body. This study aimed to investigate the magnetic and electrical properties of three materials: soil, paperboard, and cloth, and to study the brain activity further when prostrating on these three different materials.

This is a cross-sectional study.The participants included 10 (4 women and 6 men) healthy individuals aged 20-40, who voluntarily participated in this project.In the first part, the physical examination of different materials was done. The vibrating sample magnetometer was used to check the magnetic permeability, and a two-point electrical conductivity measuring device was used to check the electrical conductivity of the soil, paperboard, and cloth samples. In the second part of the study, brain waves of ten healthy peoplein the sitting position, prostration on the soil, paperboard, and carpet with the same conditions was recorded. The relative strengths of the frequency bands were investigated. The significance level was considered after calculating the false discovery rate to delete the channels and bands effect interface.In the present study, all the ethical considerations were observed and the authors reported no conflict of interests.

The results of the first part showed that paperboard had more magnetic permeability than cloth and cloth had more magnetic permeability compared to soil and had less electrical conductivity as well. In the second part, the results showed that paperboard and soil caused a significant increase in the relative power of the alpha 1 band in the frontal and central areas compared to the sitting position, while there was no significant difference in prostration on the carpet. Prostration on the carpet significantly increased the relative power of the gamma band 1 compared to sitting, while prostration on the soil, especially paperboard, showed this difference to a lesser extent.

Based on the findings, it can be stated thatprostration on paperboard increased, and prostration on carpet diminished the effects of prostration on the soil.

The main purpose of this research is to explore and analyze changes in various frequency bands of brain signals among two distinct groups: memorizers and non-memorizers of the Quran. This investigation focuses on the execution of visual memory tests using Cantab software, with an emphasis on selecting optimal feature channels and employing different classifiers.

First, brain signals were recorded from 15 Quran memorizers and 15 non-Quran memorizers during the performance of delayed matching to sample (DMS), Paired Associates Learning (PAL), and Spatial Recognition Memory image memory tests using Cantab software. Following appropriate pre-processing, non-linear features such as Lyapunov profile, correlation dimension, entropy, and detrended fluctuation analysis parameters were extracted. The selection of relevant channels was performed using T-TEST, Sequential Forward Selection, and Genetic Algorithm (GA) methods. Classification involved the use of multi-layer perceptron (MLP), Support Vector Machine, and naïve Bayes algorithms.

The selected optimal channels were primarily associated with frontal, parietal, and occipital brain regions involved in the attention network and visual memory of Quran memorizers. In most instances, the average power of low-frequency components in brain signals was found to be higher in memorizers than in non-memorizers. The MLP neural network, utilizing optimal channels selected by the GA method, demonstrated the highest accuracy between memorizers and non-memorizers at 94.79%.

Analysis of EEG data revealed that the power ratio of low-frequency components, the power ratio of low-to-high-frequency components, and the power ratio of theta to beta bands indicated an increase in relaxation and patience among the memorizer group during the retrieval phase of visual memory. This enhanced concentration and attention, leading to a higher percentage of correct answers and increased reaction time in the memorizer group during the implementation of visual memory tests using Cantab software. The MLP neural network, employing features selected by the GA method, particularly sample and approximate entropy in D, A5 sub-bands, and in the occipital, parietal, and central brain regions, achieved a superior accuracy percentage in the implementation of the DMS test.

An unexpected number of people are at risk of Alzheimer's disease. Therefore, efforts to find effective preventive measures require to be intensified.

To diagnose Alzheimer's disease through EEG signals using an artificial neural network, the first step involves pre-processing the recorded raw EEG data. This pre-processing includes the application of a 0.5 to 45 Hz bandpass filter to eliminate interference from the city's electrical signals. From the pre-processed data, the feature will be extracted. These features are related to time and frequency domains. Fourier transform, wavelet, first component analysis, nonlinear features of entropy, correlation dimension, and fractal dimension are among the suggested features. The extracted features will be evaluated by analysis of variance or t-test. The features that had the ability to separate different classes and have better statistical distribution in variance analysis or t-test are selected.

According to the capabilities of the artificial neural network in identifying different patterns and categorizing information that is set during a learning process, in this research, the artificial neural network will be used to determine the nonlinear mapping between EEG signals and the diagnosis of Alzheimer's disease. The database was divided into two categories: training and testing. In other words, the artificial neural network with the characteristics of the recorded signals as input and sick or healthy as the output of the neural network, and finally the the output of the trained artificial neural network is the diagnosis of sick or healthy data. In the final stage, the performance of the developed neural network will be evaluated and compared.

Utilizing both EEG signals and artificial neural networks could represent a novel method for the diagnosis of Alzheimer's Disease in its early stages.

Accurate diagnosis of Alzheimer’s disease in the early stages plays an important role in patient care, and preventive measures should be taken before irreversible brain damage occurs. With increasing age, there are changes in memory, which is normal, but the symptoms of Alzheimer’s disease are more than temporary forgetfulness. Early and intelligent diagnosis of Alzheimer’s disease in different situations can greatly help patients and physicians.

In the proposed method, a convolutional neural network will be used to improve the recognition of people with Alzheimer’s disease from healthy people in emotional states. First, the required pre-processing is done on the electroencephalography signal, and then, it will be applied as an input to the network. Next, the genetic algorithm is used to optimize the weights of the convolutional neural network.

The research shows that the frontal lobe of the brain is related to emotions and the use of F3 and F4 channels reflects more information compared to other channels, so with this information, the process of recognizing Alzheimer’s patients in emotional states is better.

The proposed method was evaluated with other categories in valence and arousal states. It was observed that this method has a better efficiency compared to other methods with an accuracy of 92.3% in valence and 94.3% in arousal in recognizing people with Alzheimer’s disease.

Air traffic control is a very complex process, including multiple human-machine interactions. Human mental workload plays an important role in this process. Nowadays, electroencephalography indexes are considered as new indicators in the field of assessment of mental workload. The purpose of the present study was to investigate the relationship between EEG theta power and mental workload in air traffic control simulation.

Fourteen air traffic controllers participated in this study. Controllers carried out two scenarios, including low and high workload, based on task load factors in an air traffic control simulator. Mental workload was assessed in these two scenarios by the NASA-TLX questionnaire. EEG signals were continuously recorded during air traffic control tasks. Afterward, absolute theta power was extracted from participants’ EEG using Fast Fourier Transform (FFT) by the MATLAB software and was compared with each other in terms of high and low workload.

The results showed a significant relationship in absolute theta power during low and high workload scenarios in all regions of the brain (p < 0.05). Absolute theta power increased primarily in the frontal region during the high workload scenario. Also, there was a significant increase in the relationship between work experience and absolute theta power at the F3 region during the high workload scenario (P=0.021, r=0.607).

Absolute theta power provides a good parameter to assess mental workload at different levels of air traffic control tasks. Therefore, it can be used as a tool for the design of human-machine complex systems.

Risk-taking is a personality trait which plays a part in the occurrence of work-related accidents. For this reason, people who are highly risk-taking whose decision might cause accident should not be employed in critical situations. The purpose of this survey was to design and verify the validity of the risk tolerance questionnaire, suitable for control room operators, through examining the event related potential (ERP).
At first, the questions were selected from reliable scientific resources based on the conceptual model. The questions of the initial questionnaire were selected based on face validity, and then the questionnaire was filled out by 178 control room operators. At the next step, the best questions of the questionnaire were extracted using exploratory factor analysis (EFA). In terms of reliability, 42 individuals of the study group refilled in the questionnaire again after three months as a test-retest. The ERPs were assessed using electroencephalography along with Balloon Analogue Risk Task (BART). The correlation coefficient calculated between the ERPs, and risky behaviors, and questionnaire scores.
One factor and 13 questions were identified as the best questions regarding EFA. Cronbach's alpha was 0.91. The Spearman correlation coefficient was calculated between the questionnaire score and risk-taking behavior as well as between the questionnaire score and P300, which was 0.38 (P = 0.01, η2 = 0.70) and 0.63 (P = 0.01, η2 = 0.99), respectively.
The Operator control Room Risk-Taking (ORTQ) questionnaire consists of 13 questions which can be used as an appropriate tool to assess the risk-taking trait in control room operators and also for research purposes. This questionnaire has got three personality dimensions including risk-taking nature, impulsivity and venturesomeness.

Recognition of mental activities in brain-computer interface systems based on motor imagery has attracted the attention of many researchers. A visibility graph is a powerful method for analyzing the function and connectivity of different areas of the brain. The aim of this study is to improve and develop the visibility graph method for analyzing brain behavior and detecting motor imagery.

First, brain signals including four motor imagery classes of left-handed, right-handed, foot, and tongue were transformed into three types of visibility graphs, and important features of these graphs were extracted. Then, to reduce features, the method of analysis of variance was used. To classify the motor imagery classes, the support vector machine was used. In most investigations, graph degree distribution has been used to extract information and graph weighting. In the present study, amplitude difference distribution has been used so shorter time series are required. To analyze the function and connectivity of different areas of the brain and to obtain the direction of information flow, a new method called weighted horizontal visibility graph-transfer entropy has been proposed.

Increasing the kappa value compared to other studies showed that a weighted horizontal visibility graph is a suitable method for processing brain signals based on motor imagery. A comparison of brain graphs and the direction of information flow in the four classes of motor imagery showed a significant difference between them.

Temporal networks provide a better understanding of brain dynamics in brain-computer interface systems based on motor imagery.

Inhaling substances are hydrocarbons that are converted to gas at room temperature and enter the lungs through the nose and mouth and then our brain. Neurological and psychiatric effects are reported following inhalation, however, there are few studies about acute effect of inhalant on brain electroencephalogram (EEG).

This observational study was performed in Quantitative Electroencephalography unit in Sari Zare Hospital, 2020. Twenty people who used nail polish containing aceton for cosmetic usages participated in this study. Brain signals were recorded before, during, at first and second three minutes after termination of using nail polish containing acetone, were recorded by mitsar 201 amplifier in eyes-open condition. The relative power of the band frequency waves in the occipital, frontal, parietal and midline lobes were calculated using Neurogide software. Data analysis was performed in STATA-15.

During acetone nail polish use and at first and second 3 minutes after that relative power in delta and beta band waves significantly increased in C3, C4, Cz, F3, F4, Fz, O1, O2, P3, P4, and Pz (P<0.01) while relative power of alpha frequency band significantly decreased (P<0.01). Theta frequency band showed no changes.

In this study, acute exposure to acetone increased relative power of delta and beta waves and reduced relative power of alpha waves, in frontal, parietal, occipital, and midline regions in both hemispheres.

The main purpose of this study is to provide a method for early diagnosis of Alzheimer's disease. The disease reduces memory function by destroying nerve cells in the nervous system and reducing nerve connections and interactions.

The level of the disease should be diagnosed according to the association of the disease with various features in the brain signal and medical images. First, with proper preprocessing, nonlinear properties such as phase diagram, correlation dimension, entropy and Lyapunov exponent are extracted and Elman neural network is used for classification. Then, the correctness of the function of Elman neural network is compared with channel neural network. The use of deep learning methods, including channel neural network, can have more appropriate and accurate results among other classification methods.

In the case of using two CNN networks and one MLP network, the accuracy of the results was %98 in healthy individuals, %97.7 in mild patients and %97.5 in severely ill patients. In the case of using a CNN network with a combination of features, brain signal and medical images, in the case of stimulation, the accuracy of the results is %95 in healthy individuals, %92.5 in mild patients and %97.5 in severe patients. As a recall, the accuracy of the results is %75 in healthy individuals, %72.5 in mild patients and %87.5 in severe patients. The accuracy of the results in Elman neural network with the combination of brain signal features and medical images is %94.4 and in the case without combination of features, the accuracy of the results is %92.2.

Among the processing methods proposed to classify the three classes of healthy, mild patient and severe patient, the method of combining brain signal characteristics and medical images has increased the accuracy of CNN and Elman classifier results.

Attempting to recognize specific QEEG markers in depression and obsessive-compulsive disorders is the one of main interests of research in quantitative electroencephalography. The purpose of the present study was to compare the absolute power of brain waves in the frontal area in people with major depressive disorder and obsessive-compulsive disorder.

The method of this study is causal-comparative. The statistical population of this study consisted of all individuals with major depressive disorder and obsessive-compulsive disorder referring to the mental health Clinic of Fatemi Hospital in 2019 in Ardabil, Iran. 15 people with major depressive disorder and 15 subjects with obsessive-compulsive disorder were selected by purposeful sampling. Furthermore, 15 normal individuals were selected via the sampling method from the relatives of patients. Psychiatric diagnosis and structured clinical interview, Beck depression inventory, and Foa et al. obsessive-compulsive inventory were used to collect data. The QEEG recording was performed at the Psychological Laboratory of Mohaghegh Ardabili University and the data were analyzed by Neuroguide software.

The results showed that the absolute power of delta (F= 3.444), theta (F= 51.566), alpha (F= 217.1144), and beta (F= 175.555) waves differ between people with depressive disorder and obsessive-compulsive disorder compared to the control group. The delta, theta, and alpha absolute power at frontal lobes of patients with obsessive-compulsion significantly increased, and the alpha and beta absolute power at frontal lobes of patients with major depressive disorder significantly decreased compared to the control group.

These results showed that the pattern of brain waves can be posed as an index for diagnosing and follow-upping of the therapeutic outcomes of major depression and obsessive-compulsive disorders. Furthermore, it can be used in designing neurofeedback interventions for these disorders.

Recent evidence indicates that lighting can affect physiological and neurobehavioral human functions, referred to as non-image forming (NIF) effects of lighteffects. This study aimed to determine the effects of illumination levels and correlated color temperature (CCT) on alertness and performance under NIF conditions.

In this study, 22 participants were exposed to light at various levels of illumination, including very low illuminance (<5 lux, control) and light conditions with correlated color temperatures (CCT) of 4000 K, 8000 K, and 12000 K at 300 and 500 lux. The data collection process included recording the power of brainwaves (alpha, alpha-theta, beta, and theta), sleepiness and mood scales, and cognitive performance tests for sustained attention, psychomotor vigilance task (PVT), working memory, and inhibitory capacity.

The results indicated that 500 lux light conditions at correlated color temperatures of 8000 and 12000K significantly reduced normalized alpha, alpha-theta power, subjective sleepiness, and performance compared to the dim light condition. Under illumination levels of 300 and 500 lux, there were no significant differences in the measured values of the variables.

Lighting interventions can be used as a supplement to other strategies for increasing alertness and performance in the workplace.

Electroencephalography is one of the non-invasive and relatively inexpensive methods that can be used to evaluate neurophysiology and cognitive functions. This systematic review study was performed with the aim of using electroencephalography (EEG) in ergonomics.

In this review study, all articles published in Persian and English on the application of electroencephalography (EEG) in ergonomics from March 20, 2010 to March 21, 2021 were reviewed. For this purpose, a systematic search of articles was performed using the keywords cognitive ergonomics, mental fatigue, electroencephalography, EEG and brain waves in the databases of PubMed, Google Scholar, Web of science, SID, Scopus, Magiran Iran Medex.

Most studies were conducted between 2015 and 2020 (41 papers) and most of the subjects were car drivers. Selected articles were reviewed in seven areas of mental fatigue, mental workload, mental effort, visual fatigue, working memory load, emotions, stress, and error diagnosis. The journal Perceptual and Motor Skills, followed by Applied Ergonomics, published the largest number of related articles.

In the reviewed articles, the assessment of a person's mental states, especially when driving a vehicle, has been further studied and through it, tracking, monitoring and various tasks of working memory have been followed. Future research should focus on the use of computational methods that take into account the dynamic and unstable nature of EEG data. Such an approach could facilitate the development of fatigue detection systems and automated adaptive systems.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects information processing in the nervous system and the procedure of natural brain evolution. Therefore, the processing and analysis of the brain function of these patients have captivated the attention of many researchers. Qualitative electroencephalography (EEG) is an evaluation method for determining functional brain abnormalities that is different from quantitative EEG. Chaotic tools are used in qualitative EEG whereas linear and nonlinear methods are applied in quantitative EEG. The purpose of the present study is to compare qualitative EEG findings of healthy and autistic subjects.

In this study, 19 channels of brain signals (Cz, C4, F4, Fz, F3, C3, P3, Pz, P4, T4, F8, Fp2, Fp1, F7, T3, T5, O1, O2, T6) of 6 healthy and 5 autistic subjects were evaluated in two phases. At first 5-minutes with closed eyes and then 5-minutes with opened eyes, the subjectchr('39')s EEG was recorded. After removing the artifacts, the correlation dimension of the signals was calculated, and brain maps were plotted to analyze the changes of correlation dimension on the scalp surface.

By comparison of the brain maps of the healthy and autistic groups between the opened and closed eyes periods, we found there was a difference between the brain function of the groups, especially in the T3 and T4 regions of the temporal regions as well as frontal and posterior areas.

Using brain maps, correlation dimension mapping on the brain surface provides a better understanding of brain dynamics in autistic subjects.

Electroencephalography (EEG) is the most commonly used method to study the function of the brain. This study represents a computerized model for distinguishing between epileptic and healthy subjects using EEG signals with relatively high accuracy.

The EEG database used in this study was obtained from the data available in Andrzejak. This dataset consists of 5 EEG sets (designated as A to E), each containing 100 EEG sections. Collections A and B comprised EEG signals that have been taken from 5 healthy volunteers. The C and D sets referred to EEGs from patients with focal epilepsy (without ictal recordings) and the E set was derived from a patient with ictal recording. Support vector machines were used after applying principal components analysis or linear discriminant analysis over the features of the signals. MATLAB has been used to implement and test the proposed classification algorithm. To evaluate the proposed method, the confusion matrix, overall success rate, ROC, and the AUC of each class were extracted. K-fold cross-validation technique was used to validate the results.

The overall success rate achieved in this study was above 82%. Dimension reduction algorithms can improve its accuracy and speed.

It is helpful to be able to predict the occurrence of a seizure early and accurately. Using the computerized model represented in this study could accomplish this goal.

Evaluation of the electroencephalogram of schizophrenic patients has been the subject of many recent studies. However, accurate diagnosis of schizophrenia using the electroencephalogram is still a challenging issue.

This paper was aimed to investigate the information discrepancy between one brain channel and other electrodes in two groups of schizophrenic patients and healthy individuals. Furthermore, the capability of the extracted features in the problem of classification of the two groups was investigated.

In the present analytic observational study, 19 channels of the electroencephalogram of 14 patients with schizophrenia (7 males with an average age of 27.9 ± 3.3 years and 7 females with a mean age of 28.3 ± 4.1 years), who were hospitalized at the Institute of Psychiatry and Neurologists in Warsaw, Poland, were used. In addition, data from 14 healthy individuals (7 males and 7 females with an average age of 26.8 ± 2.9 and 28.7 ± 3.4 years, respectively) were analyzed as a control group. Cross information potential and Cauchy-Schwartz mutual information between each electroencephalogram electrode and all the other electrodes were calculated. Using two strategies, the performance of the support vector machine was evaluated: (1) the mutual information of an electroencephalogram channel with other channels, and (2) the combination of the mutual information of all brain channels.

The results showed that using mutual information between electroencephalogram channels, the diagnosis accuracy increases up to 100%. For both indices, the mutual information between O2 and the other channels provided the highest classification performance.

These results nominated the proposed system as a superior one compared to the state-of-the-art electroencephalogram schizophrenia diagnosis tools.

Smoking is considered a gateway to drug use and other risky behaviors. This study aimed to investigate the effect of images (neutralized and warning) on tobacco product packaging on brain wave patterns of smokers.

A semi-experimental study was designed. The statistical population included all male students of Tabriz University in 2017-18. The alpha-peak, theta, alpha, SMR, and beta frequency bands of these individuals were quantitatively recorded. Data were then analyzed by repeated-measures analysis using SPSS version 21 software.

The results showed that the main effect of recording position (P <0.001), the main effect of the frequency band (P <0.001), and the interactive effect of recording position and frequency band (P <0.001) were significant. Warning images were more effective than neutral images. The mean beta was 10.21 for warning images, 8.03 for neutral images, and 7.25 for resting.

Designers should revise the structure of alert messages on cigarette packs such as the plot, size, and type of images, reform items such as repeating the message image and neutralizing the image, and pay low attention to the attractiveness of the brand.