Neural network based classification of human emotions using Electromyogram signals

Author

Latha, G. Charlyn Pushpa ; Hema, C.R. ; Paulraji, M.P.

Author_Institution

Fac. of Eng., Karpagam Univ., Coimbatore, India

fYear

2013

fDate

19-21 Dec. 2013

Firstpage

1

Lastpage

4

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%.

Keywords

electromyography; emotion recognition; face recognition; feature extraction; image classification; neural nets; psychology; FEMG signals; bandpower feature extraction; electromyogram signals; emotion identification; facial electromyography; facial expression identification; facial expression of emotion; neural network based human emotion classification; Accuracy; Biological neural networks; Conferences; Electromyography; Emotion recognition; Feature extraction; Bandpower; Electromyography; Elman Neural Network; Facial Electromyography; Feed Forward Neural Network;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Computing and Communication Systems (ICACCS), 2013 International Conference on

Conference_Location

Coimbatore

Type

conf

DOI

10.1109/ICACCS.2013.6938762

Filename

6938762