Browsing by Author "Tantawi, Manal"

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    Comparing Multi-class Approaches for Motor Imagery Using Renyi Entropy
    (Springer International Publishing, 2019) Selim, Sahar; Tantawi, Manal; Shedeed, Howida; Badr, Amr
    One of the main problems that face Motor Imagery-based system is addressing multi-class problem. Various approaches have been used to tackle this problem. Most of these approaches tend to divide multi-class problem into binary sub problems. This study aims to address the multi-class problem by comparing five multi-class approaches; One-vs-One (OVO), One-vs-Rest (OVR), Divide & Conquer (DC), Binary Hierarchy (BH), and Multi-class approaches. Renyi entropy was examined for feature extraction. Three linear classifiers were used to implement these five-approaches: Support Vector Machine (SVM), Multinomial Logistic Regression (MLR) and Linear Discriminant Analysis (LDA). These approaches were compared according to their performance and time consumption. The comparative results show that, Renyi entropy demonstrated its robustness not only as a feature extraction technique but also as a powerful dimension reduction technique, for multi-class problem. In addition, LDA proved to be the best classifier for almost all approaches with minimum execution time.
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    Reducing Execution Time for Real-Time Motor Imagery Based BCI Systems
    (SPRINGER INTERNATIONAL PUBLISHING AG, 2017) Selim, Sahar; Tantawi, Manal; Shedeed, Howida; Badr, Amr
    Brain Computer Interface (BCI) systems based on electroencephalography (EEG) has introduced a new communication method for people with severe motor disabilities. One of the main challenges of Motor Imagery (MI) is to develop a real-time BCI system. Using complex classification techniques to enhance the accuracy of the system may cause a remarkable delay of real-time systems. This paper aims to achieve high accuracy with low computational cost. Two public datasets (BCIC III IVa and BCIC IV IIa) were used in this study; to check the robustness of the proposed approach. Dimension reduction of input signal has been done by channel selection and extracting features using Root Mean Square (RMS). The extracted features have been examined with four different classifiers. Experimental results showed that using Least Squares classifier gives best results, compared to other classifiers, with minimum computational time.