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Tytuł:
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Emotion Analysis Using Electrodermal Signals and Spiking Deep Belief Network.
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Autorzy:
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Ganapathy N; Non-Invasive Imaging and Diagnostics laboratory, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India - 600036.
Swaminathan R; Non-Invasive Imaging and Diagnostics laboratory, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India - 600036.
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Źródło:
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Studies in health technology and informatics [Stud Health Technol Inform] 2020 Jun 16; Vol. 270, pp. 1269-1270.
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Typ publikacji:
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Journal Article
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Język:
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English
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Imprint Name(s):
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Original Publication: Amsterdam ; Washington, DC : IOS Press, 1991-
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MeSH Terms:
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Arousal*
Emotions*
Humans
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Contributed Indexing:
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Keywords: Electrodermal Activity; Emotion; Spiking Deep Belief Network
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Entry Date(s):
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Date Created: 20200624 Date Completed: 20200820 Latest Revision: 20200820
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Update Code:
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20240105
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DOI:
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10.3233/SHTI200396
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PMID:
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32570613
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In this work, an attempt has been made to discriminate arousal and valence dimensions in Electrodermal Activity (EDA) signals using Spiking Deep Belief Network (SDBN). EDA signals having different arousal and valence dimensions are obtained from public online database. These signals are divided into equal parts and normalized using channel normalization. Later, signals are subjected to SDBN for event-related features and classification. A leave-one-out cross validation is used to investigate the classification performance. The result shows that the SDBN classifiers are able to discriminate the emotional states. The network yields better classification performance for emotional dimensions of arousal and valence. It appears that the proposed approach can be used to differentiate autonomic and pathological conditions.
