Sharif Digital Repository

Since recording first electroencephalogram (EEG) of human brain in 1929 until now it becomes as a powerful tool in neuroscience. At first information extraction was done by visionary approaches only. But because of some problems in the context of inaccuracy and also in analyzing data different methods were proposed in order to extract hidden information of EEG. Among these approaches Fourier transformation was suggested as a very useful method that could draw out so many characteristics of signal different frequency components. However this way had many faults that cause limitation in analyzing time series and as a result other methods have been considered. One method that later has been... 

Make a connection between brain and computer, or Brain Computer Interface (BCI) for broad applications in areas such as medical and gamming has caused the subject to one of the most important and attractive issues in recent decades. From the perspective of pattern recognition, BCI is a classification issue that should receive signals that relate to the certain decisions of the brain and then after processing, it is concluded that the person has thought to what decision. Decisions that taken by individual, is sent from the brain to the body by signals, which is called Electroencephalogram (EEG). The number of these decisions is further, classified it also becomes more difficult. That is why... 

The person identification by Electroencephalographic (EEG) signals has attracted the researchers’ great attention in recent years and lots of investigations have been developed. An identification system seeks to identify a person in a database. The advantage of using EEG signals for person identification is the difficulty in generating artificial signals for imposters. But more works need to be done to use EEG based biometric in real-life and this thesis is one of them. In this project we classify the EEG signals for person identification using AdaBoost algorithm. Adaptive boosting (AdaBoost) is a machine learning technique for pattern classification in which the performance of the weak... 

Epilepsy is a neurological disorder whose prevalence is estimated to be 1% of the world population. Electroencephalogram (EEG) is one of the best and convenient non-invasive tools used in diagnosis and analysis of this disease. Epileptic components extracted from EEG recordings are widely used in neuroscience in the diagnosis analysis like epilepsy source localization. However, epileptic components are often contaminated and covered with artifacts of physiological origin (baseline, EMG, ECG, EOG, etc.) or instrument noises (power supply, electrode, etc.). So, preprocessing and denoising is necessary for precise analysis of epilepsy EEG recording. Heretofore, several methods have been... 

Tinnitus is a perception of sound in the absence of an external source. Tinnitus is commonly associated with the hearing system and the sound perception areas in brain. Subjective tinnitus etiology has not been fully understood. Recent researches introduce some theories by means of finding structural differences in brain of tinnitus patients in comparison to normal people. In most previous researches on finding correlations of tinnitus in EEG signals, processes has been performed in time or frequency domain. According to these evidence and great performance of time-frequency analyses tools, especially wavelets in processing non-stationary signals, we utilized wavelet packets to decompose EEG... 

It is believed that the synchronized activity of different brain areas, is the main cause of information binding inside the brain. Tis is definitely one of the most exciting challenges in modelling modern complex systems. Brain disorders such as schizophrenia,Alzheimer’s disease, epilepsy, autism and Parkinson’s disease are associated with abnormal synchronization abilities of neural networks. Functional connections can be assessed indirectly by measuring the electrophysiological criteria of ynchronization.Traditionally, in the study of neurophysiological, synchronizations are assessed by analyzing the coherence of frequency-domain characteristics of time series in standard methods for... 

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... 

Tinnitus is known as a disorder in which a person is heard a sound without an external source. The tinnitus with brain disorder source is still unknown and in recent years, various experiments have proposed many hypotheses and tried to evaluate the structural differences of the brain in tinnitus with normal people. The combination of Transcranial Magnetic Stimulation (TMS) with ElectroEncephaloGram (EEG) provides a good evaluation system with good time resolution and fair spatial resolution. In this study, at first, based on a wide range of studies in the field of TMS on Tinnitus have been examined from the participants conditions and stimulation parameters aspects to suitable stimulation... 

In the process of recording electrical activity of the brain, the signal of interest is usually contaminated with different activities arising from various sources of noise and artifact such as muscle activity. This renders denoising as an important preprocessing stage in some ElectroEncephaloGraphy (EEG) applications such as source localization. In this thesis, we propose six methods for noise cancelation of epileptic signals. The first two methods, which are based on Generalized EigenValue Decomposition (GEVD) and Denoising Source Separation (DSS) frameworks, are used to denoise interictal data. To extract a priori information required by GEVD and DSS, we propose a series of preprocessing... 

Video games, as a form of entertainment, have gained widespread attention and usage among all age groups, especially children and adolescents. With a wide variety of game genres and difficulty levels, they offer the opportunity to assess cognitive performance in individuals based on inter-individual differences and variable characteristics such as age, gender, and literacy level. The aim of this research is to study the brain response and gaze dynamics of individuals in a computer game (endless runner) based on electroencephalogram (EEG) signals and eye tracker data. The research process consists of two phases: "Brain Signal Processing in Motor Imagery Tasks" and "Reward and Punishment... 

Complex systems are composed of a large number of subsystems behaving in a collective manner. In such systems, which are usually far from equilibrium, collective behavior arises due to self-organization and results in the formation of temporal, spatial, spatio-temporall structures. Examples of complex systems are turbulent flow, stock markets, dynamics of a brain, etc. In study of the complex systems, we always encounter with handling and analysing of a Big-Data set. There are several approaches to overcome this problem, among which the most powerful method is the clustering analysis. Clustering algorithm is based on the classifying of dynamics of complex system using some similarity... 

Motor imagery causes Event related Synchronization/Desynchronization (ERS/ERD) in Electroencephalogram (EEG) signal. These potentials can be used as an input for a Brain Computer Interface (BCI) system. To do so, it is necessary for these inputs to be correctly classified. The quality of classification is severely affected by the features extracted. Common Spatial Patterns (CSP) algorithm is often used for this task. Some of this method disadvantages are neglecting non-stationary properties of EEG signal and its proneness to overfitting. Additionally, its success is highly dependent on the frequency band that the algorithm is performed in. The most suitable sub band is interchangeable... 

The outlook of brain-computer interfacing (BCI) is very bright. The real-time, accurate detection of a motor movement task is critical in BCI systems. The poor signal-to-noise-ratio (SNR) of EEG signals and the ambiguity of noise generator sources in brain renders this task quite challenging. In this thesis, we demonstrate a novel algorithm for precise detection of the onset of a motor movement through identification of event-related-desynchronization (ERD) patterns. Using an adaptive matched filter technique implemented based on an optimized continues Wavelet transform by selecting an appropriate basis, we can detect single-trial ERDs. Moreover, we use a maximum-likelihood (ML),... 

Among the most costly diseases that affect a person's quality of life throughout his or her life, mental disorders (excluding sleep disorders) affect up to 25 percent of people in any community. One of the most common types of these disorders in Iran is depressive disorder, which according to official statistics, 13% of Iranians have some symptoms of it. Until now, the diagnosis of this disease has been traditionally done in clinics with interviews and questionnaires tests based on behavioral psychology and using symptom assessment. Therefore, there is a relatively low accuracy in the treatment process. Nowadays, with the help of functional brain imaging such as electroencephalogram (EEG)... 

Sleep is vital for physical and mental health, affecting neurocognitive, physiological, and psychopathology functions and performance. Arousals are linked with sleep and interrupt the sleep states, forming a sleep/arousal loop. Spontaneous arousals are part of a normal sleep/wake cycle. There are also different clinical conditions causing sleep fragmentation and arousals, including sleep apnea (obstructive, central, and mixed apnea), hypopnea, and non-apnea such as respiratory effort-related arousals (RERA), snoring, teeth grinding, and periodic leg movement.This research introduced a novel approach for automatic arousal detection inspired by extracting respiratory information from EEG... 

Complex systems involve a large number of degrees of freedom and consist of many components. Interactions of these components with each other, or with an external force, play a significant role in the collective behavior of the complex system.We come across complex systems in many different fields of study including neuroscience, climatology, studying stock markets, etc. The non-linearity of the interactions between their components is what they have in common. Interesting macro-scale properties can be observed in a complex system, as a result of the collective behavior of the system components. We usually focus on studying a group of components in a system, rather than a single component,... 

EEG Signal Processing has really important role in study of nerves system and nervous diseases. In most applications, signals occur in the frequency band ranging below 100 Hz are considered. In this study, the higher frequency components of the signal, especially in patients with epilepsy, reported which they are called high frequency oscillations (HFOs). Some sources considered that HFOs are up to 1000 Hz and some other considered up to 2500 Hz. In study of HFOs, the high frequencies between 100 and 200 Hz that are called ripple and seen in normal mode are separated from the faster frequencies from 200 to 500 Hz and these components can accurately help to detect epileptic foci generator (to... 

Nowadays, Electroencephalogram (EEG) is the most common method for brain activity measurement. Event Related Potentials (ERP) which are recorded through EEG, have many applications. Detecting ERP signals is an important task since their amplitudes are quite small compared to the background EEG. The usual way to address this problem is to repeat the process of EEG recording several times and use the average signal. Though averaging can be helpful, there is a need for more complicated filtering. Blind source separation methods are frequently used for ERP denoising. These methods don’t use prior information for extracting sources and their use is limited to 2D problems only. To address these... 

In this study Donepezil effect on the brain functional connectivity investigated. In order to construct the brain functional network, EEG artifacts must firstly be removed because this step has important effects on the final interpretation of the results. Therefor, a new artifact removing method is proposed and better performance of the proposed method compared to other existing methods is stated using quantitative evaluations. After artifact removal, the functional brain network is extracted using conventional methods that were applied in the similar previous studies. The reasons for using conventional methods are their simplicity and reliability. Furtheremore, to study the recent... 

Event related potentials (ERPs), are potentials that arise from the occurrence of an event in the electroencephalogram signals and have very small amplitude compared to the Electroencephalogram (EEG) signal. For that reason, to access ERPs, the experiment is repeated several times under similar conditions and then the are extracted by synchronized averaging, but in this way information such as Amplitude and Delay (Lag) which reflect Mental fatigue and Task habituation of subject is disappeared. Many methods for extracting the ERP components from the EEG signals have been presented as matrices. However, due to the twodimensional information (time and space) available, resource extraction is...