Neural network based classification of human emotions using Electromyogram signals

Facial expression of emotion is of great interest to many researchers. Facial Electromyography (FEMG) is used for the identification of different facial expressions namely happy, sad, fear, neutral, surprise etc. In this paper, a simple algorithm to identify six emotions using the FEMG signals is pr...

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Published in2013 International Conference on Advanced Computing and Communication Systems pp. 1 - 4
Main Authors Latha, G. Charlyn Pushpa, Hema, C. R., Paulraji, M. P.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2013
Subjects
Accuracy
Bandpower
Biological neural networks
Conferences
Electromyography
Elman Neural Network
Emotion recognition
Facial Electromyography
Feature extraction
Feed Forward Neural Network
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DOI10.1109/ICACCS.2013.6938762

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Abstract Facial expression of emotion is of great interest to many researchers. Facial Electromyography (FEMG) is used for the identification of different facial expressions namely happy, sad, fear, neutral, surprise etc. In this paper, a simple algorithm to identify six emotions using the FEMG signals is proposed. FEMG signals are recorded from seven subjects. The six emotions are identified using bandpower features extracted from the raw FEMG signals and neural networks. In this study, two networks are used to identify the emotions. The network has an average classification accuracy of 94.32%.
AbstractList Facial expression of emotion is of great interest to many researchers. Facial Electromyography (FEMG) is used for the identification of different facial expressions namely happy, sad, fear, neutral, surprise etc. In this paper, a simple algorithm to identify six emotions using the FEMG signals is proposed. FEMG signals are recorded from seven subjects. The six emotions are identified using bandpower features extracted from the raw FEMG signals and neural networks. In this study, two networks are used to identify the emotions. The network has an average classification accuracy of 94.32%.
Author Latha, G. Charlyn Pushpa
Hema, C. R.
Paulraji, M. P.
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  givenname: C. R.
  surname: Hema
  fullname: Hema, C. R.
  email: hemacr@karpagam.ac.in
  organization: Fac. of Eng., Karpagam Univ., Coimbatore, India
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  givenname: M. P.
  surname: Paulraji
  fullname: Paulraji, M. P.
  email: paul@unimap.edu.my
  organization: Sch. of Mechatron. Eng., Univ. Malaysia Perlis, Arau, Malaysia
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Snippet Facial expression of emotion is of great interest to many researchers. Facial Electromyography (FEMG) is used for the identification of different facial...
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SubjectTerms Accuracy
Bandpower
Biological neural networks
Conferences
Electromyography
Elman Neural Network
Emotion recognition
Facial Electromyography
Feature extraction
Feed Forward Neural Network
Title Neural network based classification of human emotions using Electromyogram signals
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