Sharif Digital Repository

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... 

The Brain Computer Interface (BCI) translates brain signals into a series of commands, enabling individuals to fulfill many of their basic needs without physical activity. Electroencephalogram (EEG) signals are commonly used as input for BCI systems, because the recording of this signal is non-invasive, inexpensive, and also have an acceptable time resolution. One of the most prevalent methods in BCI systems is the brain-computer interface based on Steady State Visual Evoked Potentials (SSVEP). These systems provide high response speed and Information Transfer Rate (ITR) as well as a good signal-to-noise ratio (SNR). The main purpose of these systems is to detect the frequency of SSVEP in... 

Tinnitus is variety of sounds heard when no corresponding external sound is present. To remedy this condition, various methods have been proposed, however none have resulted in systematic and lasting results. This study aims to investigate the efficacy of non-invasive electrical stimulation techniques (tES) in the treatment of tinnitus. Stimulation parameters, including intensity, frequency, duration, and session frequency, are tailored to the individual's subjective tinnitus characteristics and treatment goals. Specifically, transcranial direct current stimulation (tDCS) is explored as a non-invasive method of electrical stimulation. In the tDCS method, electrodes are strategically... 

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,... 

Electroencephalogram (EEG) brain source localization carries many potential applications in systems and cognitive neuroscience, and for treatment of various neurological problems such as epilepsy. According to some recent studies, determining the spatial extent of sources and estimating their true time courses have proved challenging. This master's thesis proposes a method for localizing extended brain sources. Cortical surface parcellation has been used to reduce the dimension of the inverse problem without losing much information. The active regions are assumed to be sparse and the time course of the sources exhibits a correlation structure. The reduced dimension problem was then solved by... 

During the time that the simplest action (rest) of the human brain is active, and for integration and coordination of the brain, different parts of it are in connection with each other. This connection can be directional and directionless, which are called effective connectivity and functional connectivity, respectively. It is clear that effective connectivity shows brain function better than other connectivity due to its directionality.One of the most common ways to define effective connectivity is the use of the Multivariate Autoregressive (MVAR). The MVAR model provides the time Cause of different signals on each other, meaning that the influence of the past of a variable on other... 

EEG brain signals have gained particular attention among researchers in the field of brain signal processing due to their easy and cheap recording, high temporal resolution, and non-invasiveness. On the other hand, defects such as high vulnerability to various types of noise and artifacts have caused the main challenge before processing them to improve the signal-to-noise ratio and the interpretability of brain connectivity obtained from them. In order to solve these challenges, two important problems of "separation of desired and undesired signal subspace" and "functional and effective connectivity analysis" have been raised, respectively. In solving both problems, EEG signals are usually... 

Depression is a type of mental disorder that is characterized by the continuous occurrence of bad moods in the affected person. Studies by the World Health Organization (WHO) show that depression is the second disease that threatens human life, and eight hundred thousand people die due to suicide every year. In order to reduce the damage caused by depression, it is necessary to have an accurate method for diagnosing depression and its rapid treatment, in which electroencephalogram (EEG) signals are considered as one of the best methods for diagnosing depression. Until now, various researches have been conducted to diagnose depression using electroencephalogram signals, most of which were... 

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... 

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)... 

Human has different emotions such as happiness, sadness, anger, etc. Recognizing these emotions plays an important role in human-machine interface. Emotion recognition can be divided into approaches, physiological and non-physiological signals. Non-physiological signals include facial expressions, body gesture, and voice, and physiological signals include electroencephalograph (EEG), electrocardiograph (ECG), and functional magnetic resonance imaging (fMRI). EEG signal has been absorbed a lot of attention in emotion recognition because recording of EEG signal is easy and it is non-invasive. Analysis of connectivity and interaction between different areas of the brain can provide useful... 

Attention is one of the important aspects of brain cognitive activities, which has been widely discussed in psychology and neuroscience and is one of the main fields of research in the education field. The human sense of hearing is very complex, impactful and crucial in many processes such as learning. Human body always does several tasks and uses different senses simultaneously. For example, a student who listens to his/her teacher in the class, at the same time pays attention to the teacher, looks at a text or image, and sometimes writes a note.Using the electroencephalogram (EEG) signal for attention assessment and other cognitive activities is considered because of its facile recording,... 

Tinnitus is commonly referred to the symptom of “ringing in the ear”, and it is scientifically described as the perception of sound in the absence of an acoustic event. This symptom is powerful enough to negatively affect sleep patterns and concentration. The symptoms affect more than forty-five million people only in the US and 10 to 20% of the world population. Management and treatment of subjective tinnitus is an ongoing focus of research activities. Ample evidence suggests that the mechanism of tinnitus involves maladaptive plasticity in both classic and non-classic auditory pathway. The non-classical pathway is referred to multi-modal sensory inputs to the auditory system, limbic... 

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... 

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... 

As the fourth most common neurological disorder, epilepsy affects lots of people all around the world, some of whom have to live with unpredictable seizures uncontrollable by surgery or medication. Hence, Developing systems for detection and prediction of the epileptic seizures will help the patients to avoid the possible damages caused by sudden seizures. This study addresses the task of epileptic seizure prediction, using three different novel approaches. The first approach, which is based on anomaly detection, contains three steps: feature extraction from EEG signals, training a one-class SVM classifier, and a post-processing step. The second method exploits a recurrent neural network to... 

A Brain-Computer Interface (BCI) is a communication system that does not need any peripheral muscular activity. The huge goal of BCI is to translate brain activity into a command for a computer. One of the most important topics in the brain-computer interface is motor imagery (MI), which shows the reconstruction of subjects. The electrical activities of the brain are measured as electroencephalogram (EEG). EEG signals behave as low to noise ratio also show the dynamic behaviors.In the present work, a novel approach has been employed which is based on feature extraction with discretion wavelet transform (DWT), support vector machine (SVM), Artificial Neural Network (ANN) and Convolutional... 

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... 

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... 

Brain Computer Interface (BCI) is a communication system between human brain and a computer or a peripheral device which by recording brain signals directly would send messages and commands from the human brain to computer.According to brain activity patterns of EEG, BCIs are divided into different types. The most important of these patterns called ERP (Event Related Potentials) which appears after particular events in the EEG signal. A significant ERP pattern is P300 potential. It occurs when patient recognizes oddball stimuli. SSVEP (Steady-State Visual Evoked Potential) is another type of patterns and is response of the brain to optical stimulations with certain frequencies and a strong...