Sharif Digital Repository

In recent years, the spread of neurological disorders worldwide has been increasing, especially in developing countries. Due to the unknown function, complexity, and high importance of the brain, such disorders have been pervasive, severe, prolonged, and impose enormous costs on the individual, the family, and the community. Thus, increasing the knowledge about the brain and its areas in various activities is too vital and can facilitate the diagnosis and treatment of many different and unknown neuro- logical disorders. Different kinds of research have been done to automatically process and find the active and vital areas in various states and brain activities. The problem with most of these... 

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... 

Emotions are one of the most important issues which affects daily life and activities. On the other hand, robots play an increasing role in human life and play a fundamental role in meeting our needs. One of these basic roles is empathy and verbal interaction between the robot and human. In this research, participant's emotions were stimulated in two ways: by using OASIS and GAPED image data sets and by instructing the participants to remind about their good or bad memories. During emotional stimulation, EEG signals have been recorded for the training and testing process. The preprocessing of training data includes filtering, removing bad parts of data, removing bad channels and... 

The human brain can be described as a dynamic system with multiple subsystems interacting with one another and multi-channel observations of these subsystems are available. The modeling of a system from its observations allows us to gain insight into how its various components interact with one another and also provides intuition about the desired system. Hidden Markov Model (HMM) is a probabilistic model with hidden states that is suitable for modeling these types of systems. Multi-channel observations are available from several subsystems interacting with each other within a general system. In this case, it may be necessary to develop more comprehensive models incorporating multi-channel... 

The prevalence of epilepsy in the world and the need for surgery to treat patients have made it essential to locate the site of epilepsy before surgery. One method is to apply source localization algorithms to the EEG signals of epileptic patients in the ictal and interictal periods. However, because these signals are contaminated with various noises, they are challenging to interpret and require noise cancellation. Therefore, various methods have been proposed to eliminate the noise. Among these methods, a new method recently used to remove noise from the epileptic signal is Morphological Component Analysis (MCA). This method uses the basic concepts of sparse representation of signals to... 

In many neuroscience studies, the aim is to investigate the functional role of a population of neurons in response to a certain stimulus. Electrophysiology methods usually can only record from a small population of neurons and this is not sufficient for studying functional properties of the cortex. An alternative is to use functional neuroimaging methods. However, some of these methods are expensive and also they do not offer suitable spatial and temporal resolutions. Optical imaging systems can solve these problems because they are low-cost, easy to design, and also have good temporal and spatial resolutions. These systems can generate functional maps from the brain. In this study, a... 

The brain-computer interface is an advanced technology in human-machine interaction. The Speller system is a typical use of BCI, in which the target stimulation is detected by the induced signal in the brain. The most commonly speller system, the matrix Speller, has a major disadvantage, and it is Gaze-dependent. Research has proven that target-character selection in the matrix Speller is dependent on eye movement, or as referred to in technical terminology, it is gaze dependent. Therefore, the Speller matrix is not usable for users suffering from unimpaired oculomotor control. Many researchers attempted to overcome this issue, and their results led to two solutions; 1) changing the type 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... 

In recent years, techniques in articial intelligence have become an important tool in the analysis of physiological signals. While the application of machine learning techniques has proved useful in other elds, researchers have had difficulty proving its utility for the analysis of physiological signals. A major challenge in applying such techniques to the analysis of physiological signals is dealing effectively with inter-patient differences. The morphology and interpretation of physiological signals can vary dep ending on the patient. This poses a problem, since statistical learning techniques aim to estimate the underlying system that produced the data. If the system (or patient) changes... 

The main idea in this project is investigation of multidimensional recording brain signal (ECoG) for estimation of 3D arm trajectory. First we introduce a general structure with variable blocks, in this structure we have many ways to estimate hand trajectory and obtain different result. By statistical test we find the best state of this structure and apply it on other dtae set trials. Electrocorticography (ECoG) has seen recent use in this regard because it offers a higher spatiotemporal resolution than non-invasive EEG and is less invasive than intracortical microelectrodes and have been shown to contain reliable information about the direction of arm Trajectory and movements. We using... 

In this thesis, an enhanced local mean-field (MF) 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 two pioneer MF models designed by Steyn-Ross et al and Bojak & Liley, and then 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...