Sharif Digital Repository

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... 

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... 

Localization of electrical activity in the brain is one of the major problems in cognitive science and neuroscience. Indeed, Source localization is the inverse processing procedure on brain signals to estimate the location and position of resources in the human brain. Current technics for neurological imaging is included fMRI، PET، MEG and ERP. These methods is not appropriated to answer the question that when does each of different components of the brain begin their activity. The EEG signals could be useful to eliminate some of limitations of above methods. The problem with EEG signals collected from the skull is that they don’t refer directly to the location of active neurons. The... 

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... 

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... 

Dementia is one of the most common disorders among the elderly population. Statistical analyses show that among several subtypes of dementia, Alzheimer’s disease (AD) is the most frequent cause of dementia and this number is projected to increase. AD not only results in impairment of learning and memory but also in the moderate stages of illness, motor functions are profoundly disturbed, and ultimately will affect the patient's lifetime. The proposed drug treatments for this disease only reduce its progress probability. Therefore, early diagnosis for effective treatment of Alzheimer's disease is one of the critical issues in the field of dementia. This project is an effort to extract... 

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... 

Tensors are valuable tools to represent EEG data. Tucker decomposition is the most used tensor decomposition in multidimensional discriminant analysis and tensor extension of LDA (Higher Order Discriminant Analysis-HODA) is a popular tensor discriminant method used for data of ERP-based BCIs. In this Thesis we introduce a new tensor-based feature reduction technique, named Higher Order Spectral Regression Discriminant Analysis (HOSRDA), with application in P300-based BCIs. The proposed method (HOSRDA) is a tensor extension of Spectral Regression Discriminant Analysis (SRDA) and casts the eigenproblem of HODA to a regression problem and therefore overcome the probable issue of singularity of... 

Brain-computer interfaces (BCI) are systems which enable a user to control a device using only his or her neural activity. An important part of a brain-computer interface is an algorithm for classifying different commands that the user may want to execute. There are several neurological phenomena that can be used in a BCI. One of them is event related de-synchronization (ERD), which is a temporary decrease in power of the mu and beta brain waves. This phenomenon can be registered using electroencephalography (EEG) and occurs when a subject performs or imagines a limb movement. The goal of this thesis is to implement an algorithm that would be able to classify EEG signal for controlling an... 

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... 

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... 

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... 

Different perceptual, cognitive, and emotional situations results in a kind of information flow in the brain by means of coordinated neuronal oscillations. Analysing these oscillations, especially synchronizations of different brain regions, can illustrate the brain response to the aforementioned situations. In the literature, connectivity between brain regions is divided into the three groups of structural, effective, and functional, s.t. the first one referes to the connectivity between nearby regions, while the second and third ones focus on the synchronization of oscillations of arbitrary located regions. Although EEG is not the best choice for analyzing functional connectivity between... 

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... 

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... 

Brain states analysis during consciousness is emerging research in brain-computer interface(BCI). Emotion recognition can be applied to learn brain states and stages of neural activities. Therefore, emotion recognition is crucial to the analysis of brain functioning. Electrical signals such as electroencephalogram (EEG), electrocardiogram (ECG) and functional magnetic resonance imaging(fMRI) are frequently used in emotion recognition researches. Convenience in recording, non-invasive nature and high temporal resolution are the factors that have made EEG popular in brain researches. EEG can be used to identify brain region activity solely or the connectivity of various regions in time in the... 

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... 

Time perception is a concept that describes how a person interprets the duration of an event. Depending on the circumstances, people may feel that time passes quickly or slowly. So far, the understanding, comparison, and estimation of the time interval have been described using a simple model, a pacemaker accumulator, that is powerful in explaining behavioral and biological data. Also, the role of the frequency band, Contingent Negative Variation (CNV), and Event-Related Potential (ERP) components have been investigated in the passage of time and the perception of time duration. Still, the stimuli used in these studies were not melodic. Predicting is one of the main behaviors of the brain.... 

Tinnitus means hearing a sound without an external source. Despite the research done in relation to it, it is still an unknown phenomenon and no method has been introduced to treat it. Repetitive transcranial magnetic stimulation is one of the methods that its effectiveness in decreasing tinnitus sound is still under investigation. This technology is a non-invasive method to stimulate the brain, which modulates the activity of neurons by applying successive electromagnetic pulses on the scalp. Most of the studies conducted in this field have used clinical evaluations to investigate the effect of stimulation. Relying on this method alone to evaluate the effect of stimulation cannot take a... 

In most of brain studies, the primary objective is to find dipole activities, an underdetermined problem that requires additional constraints. Adequate constraints can be added by using information from other modalities. This research aims to develop a platform that combines various noninvasive modalities to improve localization accuracy. To accomplish this, two novel general approaches to combining modalities are proposed. In the first approach, the result of localizing by different methods and in different modalities are processed and combined in intervals by Dempster Shaffer's combination law. The final amount of bipolar activity is obtained by cumulating the activities obtained at...