Using Skin Conductance EKG Facial EMG to Discriminate Affect Induced by Affective Pictures

  • 朱 炳丞

Student thesis: Doctoral Thesis


Emotion is an abstract psychological condition that affects many aspects of our lives such as judgment productivity and sleep status Recognizing and managing one's own emotions is one of the critical topics of modern people There are many applications of emotion detection today However facial recognition and self-report have their shortcomings With the miniaturization of sensors the detection of physiological signals has become much more comfortable than before Skin electrical signals electrocardiograms and electrograms between the eyebrows have also been confirmed to be related to affective pictures Therefore this research aims to analyze the affective discrimination caused by affective pictures through physiological signals The pictures provided by the International Affective Picture System and similar content pictures on the Internet are used to conduct experiments with similar content pictures collected on the Internet Subjects' physiological signals are measured while looking at the affective pictures simultaneously Self-assessment Manikin will be filled after looking at pictures Then apply paired sample T-test to analyze the trend of physiological signals produced while watching pictures The result shows that women are more prone to respond to affective pictures than men in the physiological signal And the heartbeat frequency is usually more discrimination to the affective pictures than the other two signals when viewing them Affect with apparent physiological signal trends are male excitement male depression female excitement female relaxation female depression and female anxiety And after conducting the Self-assessment Manikin on pictures from the Internet it was found that the result is similar to the original data of the International Affective Picture System Pointing that their content can effectively induce affect
Date of Award2021
Original languageEnglish
SupervisorChien-Hsu Chen (Supervisor)

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