الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
Recently Noninvasive Electroencephalogram (EEG) systems are gaining much attention. Brain-computer Interface (BCI) systems rely on EEG analysis to identify the mental state of the user, change in cognitive state and response to the events. Motor Execution (ME) is a very important control paradigm. This thesis introduces a robust and useful User-Independent Hybrid Brain-Computer Interface (UIHBCI) model to classify signals from 14-EEG channels that are used to record the reactions of the brain neurons of nine subjects. Through this thesis the researchers identified relevant multisensory features of multi-channel EEG that represent the specific mental processes depending on two different evaluation models (Audio/Video) and (Male/Female). Deep Belief Network (DBN) was applied independently on the two models where, the overall achieved classification rates were better in ME classification compared to the state of art. For evaluation four models were tested, in addition to the proposed model, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Brain-Computer Interface Lower-Limb Motor Recovery (BCI LLMR) and Hybrid Steady-State Visual Evoked Potential Rapid Serial Visual Presentation Brain-Computer Interface (Hybrid SSVEP-RSVP BCI). The results indicated that the proposed model, LDA, SVM, BCI LLMR and Hybrid SSVEP-RSVP BCI accuracies for (A/V) model are 94.44%, 66.67%, 61.11%, 83.33% and 89.67% respectively, while for (M/F) model, the overall accuracies are 94.44%, 88.89%, 83.331%, 85.44% and 89.45%. from these results, the proposed model achieved superiority over the state of art algorithms in both (A/V) and (M/F) models. Electrooculography (EOG) is a method to concurrently obtain electrophysiological signals accompanying an Electroencephalography (EEG), where both methods have a common cerebral pattern and imply a similar medical significance. The most common electrophysiological signal source is EOG that contaminated the EEG signal and thereby decreases the accuracy of measurement and the predicated signal strength. In this thesis, we develop a method to improve the correction efficiency for EOG artifacts (EOAs) on raw EEG recordings: We retrieve cerebral information from three EEG signals with high system performance and accuracy by applying feature engineering and a novel Machine-Learning (ML) procedure. To this end, we use two adaptive algorithms for signal decomposition to remove EOAs from multichannel EEG signals: Empirical Mode Decomposition (EMD) and Complete Ensemble Empirical Mode Decomposition (CEEMD), both using the Hilbert–Huang Transform. First, the signal components are decomposed into multiple intrinsic mode functions. Next, statistical feature extraction and dimension reduction using principal component analysis are employed to select optimal feature sets for the Machine Learning (ML) procedure that is based on classification and regression models. The proposed CEEMD algorithm enhances the accuracy compared to the EMD algorithm and considerably improves the multi-sensory classification of EEG signals. Models of three different categories are applied, and the classification is based on a K-Nearest Neighbor (k-NN) algorithm, a Decision Tree (DT) algorithm, and a Support Vector Machine (SVM) algorithm with accuracies of 94 % for K-NN, 75 % for DT, and 69 % for SVM. For each classification model, a regression learner is used to assist as an evidence rule for the proposed artificial system and to influence the learning process from classification and regression models. The regression learning algorithms applied include algorithms based on an Ensemble of Trees (ET), a DT, and a SVM. We find that the ET-based regression model exhibits a determination coefficient R2=1.00 outperforming the other two approaches with R2=0.80 for DT and R2=0.76 for SVM.