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Enhancing emotion recognition based on physiological signals / Salma Ibrahim Aly Alhagry ; Supervised Aly Aly Fahmy , Reda Abdelwahab Elkhoribi

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Salma Ibrahim Aly Alhagry , 2018Description: 68 Leaves : charts , facsimiles ; 30cmOther title:
  • تحسين التعرف على المشاعر باستخدام الإشارات الفسيولوجية [Added title page title]
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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Computer Science Summary: Emotion is the most important component in daily interaction between people. Nowadays, it is important to make the computers understand user{u2019}s emotion who interacts with it in human-computer interaction (HCI) systems. Electroencephalogram (EEG) signals are the main source of emotion in our body. Recently, emotion recognition based on EEG signals have attracted many researchers and many methods were reported. Different types of features were extracted from EEG signals then different types of classifiers were applied to these features. In this paper, a deep learning method is proposed to recognize emotion from raw EEG signals. Long-Short Term Memory (LSTM) is used to learn features from EEG signals then the dense layer classifies these features into low/high arousal, valence, and liking. DEAP dataset is used to verify this method which gives an average accuracy of 85.65%, 85.45%, and 87.99% with arousal, valence, and liking classes, respectively. The proposed method introduced high average accuracy in comparison with the traditional features engineering methods
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Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.20.03.M.Sc.2018.Sa.E (Browse shelf(Opens below)) Not for loan 01010110076936000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.20.03.M.Sc.2018.Sa.E (Browse shelf(Opens below)) 76936.CD Not for loan 01020110076936000

Thesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Computer Science

Emotion is the most important component in daily interaction between people. Nowadays, it is important to make the computers understand user{u2019}s emotion who interacts with it in human-computer interaction (HCI) systems. Electroencephalogram (EEG) signals are the main source of emotion in our body. Recently, emotion recognition based on EEG signals have attracted many researchers and many methods were reported. Different types of features were extracted from EEG signals then different types of classifiers were applied to these features. In this paper, a deep learning method is proposed to recognize emotion from raw EEG signals. Long-Short Term Memory (LSTM) is used to learn features from EEG signals then the dense layer classifies these features into low/high arousal, valence, and liking. DEAP dataset is used to verify this method which gives an average accuracy of 85.65%, 85.45%, and 87.99% with arousal, valence, and liking classes, respectively. The proposed method introduced high average accuracy in comparison with the traditional features engineering methods

Issued also as CD

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