الفهرس 
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Abstract
Disabled People who suffer from motor neuron diseases face major problems in doing their own activities and in communicating with others in their daily life. Brain Computer Interface (BCI) systems help those disabled people to control devices such as a computer cursor, robotic limbs, wheelchairs, or spellers by only using their thoughts. BCI also provides a new mode of communication to healthy people in other non-medical applications such as computer and mobile gaming, and military affairs. Nowadays, Electroencephalogram (EEG) signals are mostly used to detect the activities of various actions within the brain as they provide rich information about brain’s electrical activity. However, EEG signals generate large amount of data, which are usually difficult to interpret and classify. Initially a dimensionality reduction technique should be applied to identify the best subset of features out of original feature space. After feature extraction and reduction, classification algorithms can be applied. During classifier training, the task is to infer a mapping between signals and classes using the labeled feature vector produced by the feature extraction stage. During the application of BCI, the task is to discriminate different types of neurophysiologic signals, then translating them into commands. These commands are used to take the appropriate action. This thesis introduces a new classification strategy based on EEG signals, which is called Fuzzy Based Classification Strategy (FBCS). FBCS minimizes the classification time by perfectly extracts the effective features of the produced EEG signals based on a set of elected electrodes. Then a new classification technique, which is called Fuzzified KNN (FKNN) Classifier, is applied to take the classification decision accordingly. FBCS uses feature reduction and electrode selection techniques to reduce the dimensionality of data to be classified, which also improves the classification accuracy. Experimental results have shown that FBCS outperforms recent classification strategies in terms of accuracy and classification time.