الفهرس 
Overview of the Brain-Computer Interface (BCIs)Only 14 pages are availabe for public view
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Abstract
1.1Introduction Brain-Computer Interface (BCI) is a communication system, which enables the user to control special computer applications by using only his or her thoughts. Different research groups have examined and used different methods to achieve this goal. Almost all of them are based on electroencaphalography (EEG) recorded from the scalp. The EEG is measured and sampled while the user imagines different things. Depending on the BCI, particular preprocessing and feature extraction methods are applied to the EEG sample of certain length. It is then possible to detect the task-specific EEG signals or patterns from the EEG samples with a certain level of accuracy. The major goal of BCI [1, 2] research is to develop systems that help disabled users to communicate with other persons to control artificial limbs or to control their environment. To achieve this goal, many aspects of BCI systems are currently being investigated. Research areas include development of new BCI applications, evaluation of control-signals (i.e. patterns of brain activity that can be used for communication), development of algorithms for translation of brain signals into computer commands, and the development and evaluation of BCI systems specifically for disabled subjects. BCI designs are very useful for completely paralyzed individuals in order to communicate with their external surrounding using brain thoughts. In this thesis, a description of a Hidden Markov Models (HMMs), Support Vector Machines (SVM) and modified Hidden Markov Models (MHMMs) to classify the EEG are recorded.
1.2 Objectives of thesis In this work, we investigate the use of HMMs, SVM and MHMMs, as nonlinear classifiers, to classify the EEG signals. In order to control some electrical appliances based on BCI for disable subjects.
1.3 Organization of the Thesis The thesis comprises six chapters.
Chapter 2 offers an overview of the EEG signals and the discussion of the human brain. The second section of this chapter discusses the Rhythmic brain activity. The different BCI approaches are discuss in the third section, flowed by the discussion of the brain activity measurements and BCI components. Chapter 3 describes six different BCI systems. Five of them are included in the literature review. The sixth, used in the present study, Adaptive Brain Interface (ABI), is described in more detail. After the BCIs are introduced they are compared to each other and provide an overview of five BCI systems based on the scalp EEG.
Chapter 4 presents the different types of the translation algorithms which used in BCI systems.
Chapter 5 presents the classification methods of EEG signals during five mental tasks (Endogenous tasks “free thinking”: internally modulated activity) using HMMs.
Chapter 6 contains how to use the HMMs, SVM and MHMMs to classify P300 EEG signals as Exogenous (evoked potentials) signals (driven by external stimuli to evoke a specific brain response).
Finally, the given results of this thesis as well as the headlines for future work suggestions are summarized in chapter 7.