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040 _aEG-GiCUC
_beng
_cEG-GiCUC
041 0 _aeng
049 _aDeposite
097 _aM.Sc
099 _aCai01.13.03.M.Sc.2020.Mo.E
100 0 _aMohamed Hamed Ahmed Mahmoud Said
245 1 0 _aEeg-based motor imagery classification using digraph fourier transforms and extreme learning machines /
_cMohamed Hamed Ahmed Mahmoud Said ; Supervised Ayman M. Eldeib , Mahmoud H. Annaby , Muhammad A. Rushdi
246 1 5 _aتصنيف أنماط التصور الحركى المبنى على إشارات المخ باستخدام تحويلات فورييه للمخططات وآلات التعلم الفائق
260 _aCairo :
_bMohamed Hamed Ahmed Mahmoud Said ,
_c2020
300 _a91 P . :
_bcharts , facsmilies ;
_c30cm
502 _aThesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Systems and Biomedical Engineering
520 _aBrain-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
530 _aIssued also as CD
653 4 _aEEG
653 4 _aExtreme learning machines
653 4 _aMotor imagery
700 0 _aAyman M. Eldeib ,
_eSupervisor
700 0 _aMahmoud H. Annaby ,
_eSupervisor
700 0 _aMuhammad A. Rushdi ,
_eSupervisor
856 _uhttp://172.23.153.220/th.pdf
905 _aAmira
_eCataloger
905 _aNazla
_eRevisor
942 _2ddc
_cTH
999 _c79290
_d79290