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dc.contributor.advisor El-Ayat, Khaled
dc.contributor.author Mikhail, Mina
dc.creator Mikhail, Mina
dc.date.accessioned 2010-05-30T12:09:41Z
dc.date.available 2010-05-30T12:09:41Z
dc.date.created 2010 Spring
dc.date.issued 2010-05-30T12:09:41Z
dc.identifier.uri http://dar.aucegypt.edu/handle/10526/723
dc.description.abstract Emotion is an important aspect in the interaction between humans. It is fundamental to human experience and rational decision-making. There is a great interest for detecting emotions automatically. A number of techniques have been employed for this purpose using channels such as voice and facial expressions. However, these channels are not very accurate because they can be affected by users' intentions. Other techniques use physiological signals along with electroencephalography (EEG) for emotion detection. However, these approaches are not very practical for real time applications because they ask the participants to reduce any motion and facial muscle movement, reject EEG data contaminated with artifacts and rely on large number of electrodes. In this thesis, we propose an approach that analyzes highly contaminated EEG data produced from a new emotion elicitation technique. We also use a feature selection mechanism to extract features that are relevant to the emotion detection task based on neuroscience findings. We reached an average accuracy of 51% for joy emotion, 53% for anger, 58% for fear and 61% for sadness. We are also, applying our approach on smaller number of electrodes that ranges from 4 up to 25 electrodes and we reached an average classification accuracy of 33% for joy emotion, 38% for anger, 33% for fear and 37.5% for sadness using 4 or 6 electrodes only. en
dc.format.medium theses en
dc.language.iso en en
dc.rights Author retains all rights with regard to copyright. en
dc.subject.lcsh Thesis (M.A.)--American University in Cairo en
dc.subject.lcsh Algebraic fields.
dc.subject.lcsh Modules (Algebra)
dc.subject.lcsh Computer science.
dc.title Using minimal number of electrodes for emotion detection using noisy EEG data en
dc.type Text en
dc.subject.discipline Computer Science en
dc.rights.access This item is available en
dc.contributor.department American University in Cairo. Dept. of Computer Science and Engineering en


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  • Theses and Dissertations [1635]
    This collection includes theses and dissertations authored by American University in Cairo graduate students.

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