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Emotion recognition from facial EMG signals using higher order statistics and principal component analysis

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Main Authors: Selvaraj, Jerritta, Murugappan, Muthusamy, Dr., Wan Khairunizam, Wan Ahmad, Dr., Sazali, Yaacob, Prof. Dr.
Other Authors: sn.jerritta@gmail.com
Format: Article
Language:English
Published: Taylor & Francis 2014
Subjects:
Facial electromyography signals (fEMG)
Higher order statistics (HOS)
Human-computer interface (HCI)
Principal component analysis (PCA)
Online Access:http://dspace.unimap.edu.my:80/dspace/handle/123456789/33864
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spelling my.unimap-338642014-04-21T01:36:01Z Emotion recognition from facial EMG signals using higher order statistics and principal component analysis Selvaraj, Jerritta Murugappan, Muthusamy, Dr. Wan Khairunizam, Wan Ahmad, Dr. Sazali, Yaacob, Prof. Dr. sn.jerritta@gmail.com murugappan@unimap.edu.my khairunizam@unimap.edu.my s.yaacob@unimap.edu.my Facial electromyography signals (fEMG) Higher order statistics (HOS) Human-computer interface (HCI) Principal component analysis (PCA) Link to publisher's homepage at http://www.tandf.co.uk/ Higher order statistics (HOS) is an efficient feature extraction method used in diverse applications such as bio signal processing, seismic data processing, image processing, sonar, and radar. In this work, we have investigated the application of HOS to derive a set of features from facial electromyography (fEMG) signals for classifying six emotional states (happy, sad, afraid, surprised, disgusted, and neutral). fEMG signals were collected from different types of subjects in a controlled environment using audio-visual (film clips/video clips) stimuli. Acquired fEMG signals were preprocessed using moving average filter and a set of statistical features were extracted from fEMG signals. Extracted features were mapped into corresponding emotions using k-nearest neighbor classifier. Principal component analysis was utilized to analyze the efficacy of HOS features over conventional statistical features on retaining the emotional information retrieval from fEMG signals. The results of this work indicate an improved mean emotion recognition rate of 69.5% from this proposed methodology to recognize six emotional states. 2014-04-21T01:36:01Z 2014-04-21T01:36:01Z 2014-04 Article Journal of the Chinese Institute of Engineers, vol. 37(3), 2014, pages 385-394 0253-3839 http://www.tandfonline.com/doi/abs/10.1080/02533839.2013.799946#.U1RxpFWSyyo http://dspace.unimap.edu.my:80/dspace/handle/123456789/33864 en Taylor & Francis
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Facial electromyography signals (fEMG)
Higher order statistics (HOS)
Human-computer interface (HCI)
Principal component analysis (PCA)
spellingShingle Facial electromyography signals (fEMG)
Higher order statistics (HOS)
Human-computer interface (HCI)
Principal component analysis (PCA)
Selvaraj, Jerritta
Murugappan, Muthusamy, Dr.
Wan Khairunizam, Wan Ahmad, Dr.
Sazali, Yaacob, Prof. Dr.
Emotion recognition from facial EMG signals using higher order statistics and principal component analysis
description Link to publisher's homepage at http://www.tandf.co.uk/
author2 sn.jerritta@gmail.com
author_facet sn.jerritta@gmail.com
Selvaraj, Jerritta
Murugappan, Muthusamy, Dr.
Wan Khairunizam, Wan Ahmad, Dr.
Sazali, Yaacob, Prof. Dr.
format Article
author Selvaraj, Jerritta
Murugappan, Muthusamy, Dr.
Wan Khairunizam, Wan Ahmad, Dr.
Sazali, Yaacob, Prof. Dr.
author_sort Selvaraj, Jerritta
title Emotion recognition from facial EMG signals using higher order statistics and principal component analysis
title_short Emotion recognition from facial EMG signals using higher order statistics and principal component analysis
title_full Emotion recognition from facial EMG signals using higher order statistics and principal component analysis
title_fullStr Emotion recognition from facial EMG signals using higher order statistics and principal component analysis
title_full_unstemmed Emotion recognition from facial EMG signals using higher order statistics and principal component analysis
title_sort emotion recognition from facial emg signals using higher order statistics and principal component analysis
publisher Taylor & Francis
publishDate 2014
url http://dspace.unimap.edu.my:80/dspace/handle/123456789/33864
_version_ 1643797318407815168
score 13.222552

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