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Eeg-based motor imagery classification using digraph fourier transforms and extreme learning machines / Mohamed Hamed Ahmed Mahmoud Said ; Supervised Ayman M. Eldeib , Mahmoud H. Annaby , Muhammad A. Rushdi

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Mohamed Hamed Ahmed Mahmoud Said , 2020Description: 91 P . : charts , facsmilies ; 30cmOther title:
  • تصنيف أنماط التصور الحركى المبنى على إشارات المخ باستخدام تحويلات فورييه للمخططات وآلات التعلم الفائق [Added title page title]
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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Systems and Biomedical Engineering Summary: Brain-computer interface (BCI) systems have been widely proposed for rehabilitation and neural control of external devices. This thesis proposes a classification method for BCI EEG signals associated with motor imagery patterns. The proposed method uses a graph Fourier transform based on a symmetric graph Laplacian for directed and undirected graph models of multi-channel EEG signals. This method shows superior performance compared to other methods. Experiments were conducted using extreme learning machines (ELM) on the dataset Ia of BCI Competition 2003. The directed and undirected graph models resulted in accuracies of 96.58% and 95.9%, respectively. This work can be extended to larger BCI multi-channel EEG classification problems. For these problems, additional vertex-domain graph features and graph transform features can be considered to reveal hidden network patterns
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Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.03.M.Sc.2020.Mo.E (Browse shelf(Opens below)) Not for loan 01010110082361000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.03.M.Sc.2020.Mo.E (Browse shelf(Opens below)) 82361.CD Not for loan 01020110082361000

Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Systems and Biomedical Engineering

Brain-computer interface (BCI) systems have been widely proposed for rehabilitation and neural control of external devices. This thesis proposes a classification method for BCI EEG signals associated with motor imagery patterns. The proposed method uses a graph Fourier transform based on a symmetric graph Laplacian for directed and undirected graph models of multi-channel EEG signals. This method shows superior performance compared to other methods. Experiments were conducted using extreme learning machines (ELM) on the dataset Ia of BCI Competition 2003. The directed and undirected graph models resulted in accuracies of 96.58% and 95.9%, respectively. This work can be extended to larger BCI multi-channel EEG classification problems. For these problems, additional vertex-domain graph features and graph transform features can be considered to reveal hidden network patterns

Issued also as CD

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