Search for: magnetoencephalography
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    MEG based Classification of Motor Imagery Tasks

    , M.Sc. Thesis Sharif University of Technology Montazeri Ghahjaverestan, Nasim (Author) ; Shamsollahi, Mohammad Bagher (Supervisor)
    BCI is an interface between brain and machine, particularly computer which translates brain signals into understandable instructions for machine. BCI records signals and determines what the subject is doing or thinking. BCI in the point of view of pattern recognition is a classification problem. For this aim, different tasks are referred to different classes. The more number of classes, the higher complexity we encounter in classification so surveying of different kinds of features, feature selection and reduction methods have highly importance. In this project we want to design a 4-class classification that each class is referred to a direction of wrist movement. During the time that the... 

    Decoding the Long Term Memory using Magnetoencephalogram

    , M.Sc. Thesis Sharif University of Technology Tavakoli, Sahar (Author) ; Fatemizadeh, Emad (Supervisor)
    Memory and recalling process has always been a basic question. Decoding the Long-Term_Memory is one of the first steps in answering this question. Since various experiments in the field of human long-term memory, was conducted. This research is motivated by a trial that in which, the Mgntvansfalvgram (MEG) has been recorded while recalling the color and orientation of a grading which is associated with an object, after the object has been shown. High accuracy in Decoding the mentioned color and direction, will be decoding the long-term memory. In order to enhance memory decoding, the research studies different classifiers such as sparse based classifiers and other popular one. It has also... 

    Using Bump Modeling in Brain Wave Analysis

    , M.Sc. Thesis Sharif University of Technology Ghanbari Garakani, Zahra (Author) ; Shamsollahi, Mohammad Bagher (Supervisor)
    In this thesis, the efficiency of bump modeling has been investigated on brain signals, in a variety of aspects including analysis, detection, classification and prediction. The aim of bump modeling is to provide an optimized representation of the signal in time-frequency domain. This would be done by discriminating oscillatory bursts from background signal and then showing them by half-ellipsoid functions called bump. Consequently, the problem of dealing with large numbers of parameters and hence complicated calculations, which are serious concerns in similar methods, can be overcome. This is in addition to the benefits of using time-frequency representation of the signal.The aim of bump... 

    Brain Decoding Across Subjects

    , M.Sc. Thesis Sharif University of Technology Nasiri Ghosheh Bolagh, Samaneh (Author) ; Shamsollahi, Mohammad Bagher (Supervisor)
    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... 

    Multimodal Brain Source Localization

    , Ph.D. Dissertation Sharif University of Technology Oliaiee, Ashkan (Author) ; Shamsollahi, Mohammad Bagher (Supervisor) ; Hajipour Sardouei, Sepideh (Supervisor)
    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... 

    MEG based classification of wrist movement

    , Article Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 ; 2009 , Pages 986-989 ; 1557170X (ISSN) ; 978-142443296-7 (ISBN) Montazeri, N ; Shamsollahi, M. B ; Hajipour, S ; Sharif University of Technology
    Neural activity is very important source for data mining and can be used as a control signal for brain-computer interfaces (BCIs). Particularly, Magnetic signals of neurons are enriched with information about the movement of different part of the body such as wrist movement. In this paper, we use MEG (Magneto encephalography) signals of two subjects recorded during wrist movement task in four directions. Data were prepared for BCI competition 2008 for multiclass classification. Our approach for this classification problem consists of PCA as a noise reduction method, ULDA for feature reduction and various linear classifiers such as Bayesian, KNN and SVM. Final results (58%-62% for subject 1... 

    Classifying Brain Activities by Deep Methods Over Graphs

    , M.Sc. Thesis Sharif University of Technology Sarafraz, Gita (Author) ; Rabiee, Hamid Reza (Supervisor) ; Manzuri, Mohammad Taghi (Supervisor)
    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... 

    Ensemble multi-modal brain source localization using theory of evidence

    , Article Biomedical Signal Processing and Control ; Volume 69 , 2021 ; 17468094 (ISSN) Oliaiee, A ; Hajipour Sardouie, S ; Shamsollahi, M. B ; Sharif University of Technology
    Elsevier Ltd  2021
    The primary aim in pre-surgical evaluations in patients with neurological disorders such as epilepsy is determining the precise location of the cortical region responsible for the malfunctions which is called source localization. Different modalities unravel different views of brain activity. Combining these complementary aspects of the brain yields more accurate source localization. In this paper, a method is proposed for combining localization methods in different modalities based on the theory of evidence, the result of some localization methods in modalities are integrated using weights in accordance to their relative performance and are combined using Dempster's rule of combination and...