Emotion Recognition Using Electrodermal Activity Signals and Multiscale Deep Convolutional Neural Network.
In this work, an attempt has been made to classify emotional states using electrodermal activity (EDA) signals and multiscale convolutional neural networks. For this, EDA signals are considered from a publicly available "A Dataset for Emotion Analysis using Physiological Signals" (DEAP) database. Th...
| Published in: | Journal of Medical Systems Vol. 45; no. 4; pp. 1 - 11 |
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| Main Authors: | , , , |
| Format: | computer program equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
Apr2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149631214&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149631214 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Apr2021 vid: 45 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 149631214 149631214 149631214 10.1007/s10916-020-01676-6 149631214 ppf: 1 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Emotion Recognition Using Electrodermal Activity Signals and Multiscale Deep Convolutional Neural Network. aug: au: Ganapathy, Nagarajan Veeranki, Yedukondala Rao Kumar, Himanshu Swaminathan, Ramakrishnan affil: Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India sug: subj: Neural Networks (Computer) Emotions Skin Physiology Human Deep Learning Descriptive Statistics Machine Learning Decision Making ab: In this work, an attempt has been made to classify emotional states using electrodermal activity (EDA) signals and multiscale convolutional neural networks. For this, EDA signals are considered from a publicly available "A Dataset for Emotion Analysis using Physiological Signals" (DEAP) database. These signals are decomposed into multiple-scales using the coarse-grained method. The multiscale signals are applied to the Multiscale Convolutional Neural Network (MSCNN) to automatically learn robust features directly from the raw signals. Experiments are performed with the MSCNN approach to evaluate the hypothesis (i) improved classification with electrodermal activity signals, and (ii) multiscale learning captures robust complementary features at a different scale. Results show that the proposed approach is able to differentiate various emotional states. The proposed approach yields a classification accuracy of 69.33% and 71.43% for valence and arousal states, respectively. It is observed that the number of layers and the signal length are the determinants for the classifier performance. The performance of the proposed approach outperforms the single-layer convolutional neural network. The MSCNN approach provides end-to-end learning and classification of emotional states without additional signal processing. Thus, it appears that the proposed method could be a useful tool to assess the difference in emotional states for automated decision making. pubtype: Academic Journal doctype: computer program equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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