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electroencephalography
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Total 94 records
Network Analysis of EEG Data of Alzheimer’s Disease
, M.Sc. Thesis Sharif University of Technology ; Jalili, Mahdi (Supervisor)
Abstract
Recently complex networks, have been widely used as a model to study the behavior of human brain. By affecting different parts the brain, Neuronal disorders change the structure of functional brain networks. Investigating these changes using different graph theory metrics can be a useful methodology for human brain analysis in health and disease. Alzheimer's disease (AD) is a neural disease causing impairment in different brain activities including memory and cognition.The aim of this study is to construct the functional brain network of 17 AD patients and 17 healthy control subjects at resting state condition and analyzing them using the theory of complex networks in order to achieve a...
Detection of Movement Related Cortical Potentials in EEG
, M.Sc. Thesis Sharif University of Technology ; Shamsollahi, Mohammad Bagher (Supervisor)
Abstract
Movement-Related Cortical Potentials (MRCPs) are a subset of Event Related Potentials (ERPs). The event that MRCPs are related to is the endogenous event of self-paced voluntary movement. Like many other ERPs, MRCPs have small amplitudes relative to the background EEG activity, making it difficult to detect them on a single-trial basis. Nevertheless, detection of MRCPs with good accuracy can be vastly benecial to automated rehabilitation systems and to Brain-Computer Interfaces. In this project, a new experimental protocol for MRCP is introduced and signals recorded using this protocol are analyzed. The protocol has been designed and recordings have been made by the author during the summer...
Hemispheric asymmetry of electroencephalography-based functional brain networks
, Article NeuroReport ; Volume 25, Issue 16 , 12 November , 2014 , Pages 1266-1271 ; ISSN: 09594965 ; Sharif University of Technology
Abstract
Electroencephalography (EEG)-based functional brain networks have been investigated frequently in health and disease. It has been shown that a number of graph theory metrics are disrupted in brain disorders. EEG-based brain networks are often studied in the whole-brain framework, where all the nodes are grouped into a single network. In this study, we studied the brain networks in two hemispheres and assessed whether there are any hemispheric-specific patterns in the properties of the networks. To this end, resting state closed-eyes EEGs from 44 healthy individuals were processed and the network structures were extracted separately for each hemisphere. We examined neurophysiologically...
Epileptic seizure detection using neural fuzzy networks
, Article 2006 IEEE International Conference on Fuzzy Systems, Vancouver, BC, 16 July 2006 through 21 July 2006 ; 2006 , Pages 596-600 ; 10987584 (ISSN); 0780394887 (ISBN); 9780780394889 (ISBN) ; Mohseni, H. R ; Maghsoudi, A ; Sharif University of Technology
2006
Abstract
The electroencephalogram (EEG) is a representative signal containing information about the condition of the brain. The shape of the wave may contain useful information about its state. However, the human observer cannot directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the EEG signal parameters, extracted and analyzed using computers, are highly useful in diagnosis. The aim of this work is to compare the different classifiers when applied to EEG data from normal and epileptic subjects. For this purpose an adaptive neural fuzzy network (ANFN) to classify normal and epileptic EEG signals is...
Unsupervised Command Detection in EEG-based Brain-computer Interface
, M.Sc. Thesis Sharif University of Technology ; Beigy, Hamid (Supervisor)
Abstract
A Brain–Computer Interface is a system that provides a direct pathway for communication between a brain and a computer device by processing signals from sensors measuring brain activity (here Electroencephalography signals). Brain signals are known to be stochastic, non-stationary, non-linear and highly noisy, Therfore Brain–Computer Interface Systems rely on signal preprocessing, feature extraction and use of machine learning methods in order to detect mental state of Brain–Computer Interface user. Current approaches addressing the problem are mainly based on supervised learning methods. In this Thesis, first some of freely obtainable datasets with motor or motor-imagery paradigms are...
EEG based Analysis and Classification of Children with Learning Disability Compared to Normal Children
, M.Sc. Thesis Sharif University of Technology ; Jahed, Mehran (Supervisor)
Abstract
Learning disability (LD) is a neurological condition that interferes with an individual’s ability to store, process, or produce information. There are different types of learning disabilities affecting reading, writing, speaking, spelling, etc. Based on a study conducted by National Center for Learning Disabilities, 2.4 million American public school students are diagnosed with learning disability. They attend school in order to learn and be successful while they do not know their learning process is different from their peers. LD diagnosis in children is especially important as such cases must be identified early enough in order to provide them with proper education.This project targets LD...
Attentive Memory Comparison between Tinnitus Group and Normal Hearing Group Using Electroencephalogram
, M.Sc. Thesis Sharif University of Technology ; Jahed, Mehran (Supervisor) ; Mahmoudian, Saeed (Co-Advisor)
Abstract
Tinnitus is understood to be a repeating sound, often in the form of a ringing in one or both ears, in the absence of any external stimulus. There is no definite scientific justification for this condition, but this complication usually occurs due to hearing loss or after aging or acute trauma. A recent community-based epidemiological study found that 17.5% of 60-year-olds and older were suffering from Tinnitus. Despite the significant outbreak and the great impact of this impairment on the quality of life of people with this condition, no definitive treatment has been provided so far. Therefore, further research in this field is of great importance. One of the tools used to carry out these...
Brain activity modeling in general anesthesia: Enhancing local mean-field models using a slow adaptive firing rate
, Article Physical Review E - Statistical, Nonlinear, and Soft Matter Physics ; Volume 76, Issue 4 , 2007 ; 15393755 (ISSN) ; Senhadji, L ; Shamsollahi, M. B ; Vosoughi Vahdat, B ; Wodey, E ; Sharif University of Technology
American Physical Society
2007
Abstract
In this paper, an enhanced local mean-field model that is suitable for simulating the electroencephalogram (EEG) in different depths of anesthesia is presented. The main building elements of the model (e.g., excitatory and inhibitory populations) are taken from Steyn-Ross and Bojak and Liley mean-field models and a new slow ionic mechanism is included in the main model. Generally, in mean-field models, some sigmoid-shape functions determine firing rates of neural populations according to their mean membrane potentials. In the enhanced model, the sigmoid function corresponding to excitatory population is redefined to be also a function of the slow ionic mechanism. This modification adapts the...
Inserting the effects of ion channels in mean field models: Application to generation of anesthetic slow waves
, Article EUROCON 2005 - The International Conference on Computer as a Tool, Belgrade, 21 November 2005 through 24 November 2005 ; Volume I , 2005 , Pages 378-381 ; 142440049X (ISBN); 9781424400492 (ISBN) ; Senhadji, L ; Shamsollahi, M. B ; Sharif University of Technology
2005
Abstract
In this paper, effects of general anesthesia on the electroencephalogram (EEC) has been modeled with an enhanced physiological mean field theory of electrocortical activity. Enhancement is done by inserting two intrinsic ion channels (IKNa and IAR) in Liley's mean field model. In addition to excitatory and inhibitory synapses, intrinsic ion channels can generate or manipulate the brain rhythms. IKNa and IAR can produce slow brain rhythms (delta band frequency) in deep levels of anesthesia. We represent the activities of each mentioned ion channels by cascading a nonlinear function and a first order low pass filter. Linearized and numerical solutions of the modified model show that the power...
EEG Brain Functional Network Analysis in Cortex Level
, M.Sc. Thesis Sharif University of Technology ; Jalili, Mahdi (Supervisor)
Abstract
Complex networks science have received tremendous attention in recent years and the brain is one of the systems to which graph theoretical tools have been applied. Alzheimer’s disease (AD) is a neurodegenerative disease affecting many of elderly population. AD changes the anatomy of the brain, which subsequently results in changes in its functions. These changes have been frequently reported in signals recorded from the brain (such as MEG, fMRI and EEG). Among these neuroimaging techniques EEG is one of the most aproprate methods for extracting functional connectivites according to high temporal resolution. In this thesis, we aimed at analyzing the properties of EEG-based functional networks...
Constructing EEG-Based Brain Functional Connectome Using Network-based Statistics
, M.Sc. Thesis Sharif University of Technology ; Jalili, Mahdi (Supervisor)
Abstract
In recent years, there have been increasing attempts to study brain connectivity. Among a number of brain mapping techniques, Electroencephalography is an easy to use and cheap method that can be used in the study of brain function. One way of understanding the intricate wiring pattern and functions of brain is to consider it as a complex network. In this approach, a graph of brain functions, based on the functional relation of recorded electric signals, is constructed and then the network is evaluated with a number of network metrics that measure its different aspect of structure. Different neurological and psychological diseases can affect the connectivity power within the brain; as a...
Classification of EEG Signals to Detect Predefined Words in Imagined Speech
, M.Sc. Thesis Sharif University of Technology ; Beigy, Hamid (Supervisor)
Abstract
Attention to the Brain Computer interfaces (BCI) because of their potentials in improving, enhancing and substituting daily task, especially in people who suffer from diseases, has been increasing in the recent years. Such systems, receive brain activities and by extracting suitable features, try to interpret the brain commands. The aim of this project is to explore the ability of electroencephalogram (EEG) signal for silent communication by means of decoding imagined speech in brain activities. The previous research results show that imagining a word in the mind causes changes in the brain signals. These changes are interchangeable among different words. As a result, discriminating between...
“Detection and Analysis of Spindle and K-complex Patterns and SWS in Sleep EEG Signals”
, M.Sc. Thesis Sharif University of Technology ; Shamsollahi, Mohammad Bagher (Supervisor) ; Molaee-Ardekani, Behnam (Co-Advisor)
Abstract
According to necessity of analysis and detection of K-complexes and Sleep Spindles patterns which are the hallmarks of the second stage of sleep, in this thesis we aimed to introduce new methods in analysis and detection of aforementioned patterns in order to improve the results of previous methods. Also, we tried to find the relation between slow oscillations and spindles activity. In this project, in order to analysis the frequency components of Sleep Spindle, Bump modeling and STFT were used. Both of these methods confirm the spindles’ 8 Hz to 15 Hz frequency band and also their time duration between 0.5-2 seconds. On the other hand, we used modified matched filtering and also bump...
Seizure Detection in Generalized and Focal Seizure from EEG Signals
, M.Sc. Thesis Sharif University of Technology ; Hajipour, Sepideh (Supervisor)
Abstract
Epilepsy is one of the diseases that affects the quality of life of epileptic patients. Epileptic patients lose control during epileptic seizures and are more likely to face problems. Designing and creating a seizure detection system can reduce casualties from epileptic attacks. In this study, we present an automatic method that reduces the artifact from the raw signals, and then classifies the seizure and non-seizure epochs. At all stages, it is assumed that no information is available about the patient and this detection is made only based on the information of other patients. The data from this study were recorded in Temple Hospital and the recording conditions were not controlled, so...
Comparative Analysis of the Effect of Gamma-band Entrainment through Auditory Stimulation in AD Patients and Healthy Controls
, M.Sc. Thesis Sharif University of Technology ; Aghajan, Hamid (Supervisor)
Abstract
As the most widespread form of mental disorders, Alzheimer’s disease (AD) remains among the main challenges in neurology and in the field of neuroscience. There are still no effective drugs to cure this disease or slow its progress, and prevention methods are still not even close to having established records. However, the onset of AD has been linked to certain dysfunctions of the oscillatory frequencies of the affected brain mainly in the gamma band. Hence, an approach to consider for reversing the damaging effects of AD could involve reviving such oscillations through stimulating the neuronal networks in the brain that are known to be the source of these oscillations. A recent research has...
Switching Kalman Filter and Its Application in State Detection in Brain Signals
, M.Sc. Thesis Sharif University of Technology ; Shamsollahi, Mohammad Bagher (Supervisor)
Abstract
There are several methods for EEG state detection, and there are still many challenges. Switching Kalman Filter (SKF) is a suitable approach for state detection, which has been used in various applications such as QRS detection in ECG signal, apnea detection using ECG signal, and also hand path detection using EEG signal. Our goal here is to use Switching Kalman Filter (SKF) in order to detect changes in EEG signal, and in particular in sleep. In other words, we want to detect Sleep Stages. Here, detecting Sleep Stages will help doctors diagnose and treat diseases. There is a Kalman Model for each Stages of Sleep in SKF, that I model it with a AR model. In addition, SKF switch is a state...
EEG-based Thought to Text Conversion Via Interpretable Deep Networks
,
M.Sc. Thesis
Sharif University of Technology
;
Rabiee, Hamid Reza
(Supervisor)
Abstract
With the advancement of technologies related to electroencephalography signals, brain and computer interfaces, the program has received much attention. This report deals with one of the new and important issues in this field, i.e. converting thought into text. In this research, the letters, words, and sentences that a person thinks or utters in his mind are decoded and converted into text based on electroencephalography signals. There is still no credible and credible information in neuroscience about whether the same patterns of neuronal activity occur in the brain when thinking about similar letters or words. However, the remarkable growth and development of deep neural networks has made...
Migraine analysis through EEG signals with classification approach
, Article 2012 11th International Conference on Information Science, Signal Processing and their Applications, ISSPA 2012, 2 July 2012 through 5 July 2012 ; July , 2012 , Pages 859-863 ; 9781467303828 (ISBN) ; Farzi, M ; Estakhrooeieh, R. R ; Samiee, F ; Shamsollahi, M. B ; Sharif University of Technology
2012
Abstract
Migraine is a common type of headache with neurovascular origin. In this paper, a quantitative analysis of spontaneous EEG patterns is used to examine the migraine patients with maximum and minimum pain levels. The analysis is based on alpha band phase synchronization algorithm. The efficiency of extracted features are examined through one-way ANOVA test. we reached the P-value of 0.0001, proving that the EEG patterns are statistically discriminant in maximum and minimum pain levels. We also used a Neural Network based approach in order to classify the EEG patterns, distinguishing between minimum and maximum pain levels. We achieved the total accuracy of 90.9 %
EEG-based functional brain networks: Hemispheric differences in males and females
, Article Networks and Heterogeneous Media ; Volume 10, Issue 1 , March , 2015 , Pages 223-232 ; 15561801 (ISSN) ; Sharif University of Technology
American Institute of Mathematical Sciences
2015
Abstract
Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. Network representation of EEG time series can be an efficient vehicle to understand the underlying mechanisms of brain function. Brain functional networks whose nodes are brain regions and edges correspond to functional links between them are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which graph theory metrics are sex dependent. To this end, EEGs from 24 healthy female subjects and 21 healthy male subjects were recorded in eyes-closed resting state conditions. The connectivity matrices were extracted using...
A new dissimilarity index of EEG signals for epileptic seizure detection
, Article Final Program and Abstract Book - 4th International Symposium on Communications, Control, and Signal Processing, ISCCSP 2010, 3 March 2010 through 5 March 2010 ; March , 2010 ; 9781424462858 (ISBN) ; Mousavi, S. R ; Vosoughi Vahdat, B ; Shamsollahi, M. B ; Sayyah, M ; Sharif University of Technology
2010
Abstract
Epileptic seizures are generated by an abnormal synchronization of neurons. Since epileptic seizures are unforeseeable for the patients, epileptic seizures detection is an interesting issue in epileptology, that novel approaches to understand the mechanism of epileptic seizures. In this study we analyzed invasive electroencephalogram (EEG) recordings in patients suffering from medically intractable focal epilepsy with a nonlinear method called, dissimilarity index. In order to detect epileptic seizures Bhattacharyya distance between trajectory matrix of reference window during an interval quite distant in time from any seizure and trajectory matrix of present window is employed to measure...