Emotion Recognition Using Spectral Feature from Facial Electromygraphy Signals for Human-Machine Interface.

Journal: Studies in health technology and informatics
Published Date: May 27, 2021

Abstract

Recognition of the emotions demonstrated by human beings plays a crucial role in healthcare and human-machine interface. This paper reports an attempt to classify emotions using a spectral feature from facial electromyography (facial EMG) signals in the valence affective dimension. For this purpose, the facial EMG signals are obtained from the DEAP dataset. The signals are subjected to Short-Time Fourier Transform, and the peak frequency values are extracted from the signal in intervals of one second. Support vector machine (SVM) classifier is used for the classification of the features extracted. The extracted feature can classify the signals in the valence dimension with an accuracy of 61.37%. The proposed feature could be used as an added feature for emotion recognition, and this method of analysis could be extended to myoelectric control applications.

Authors

  • Jayendhra Shiva
    Instrumentation and Control Engineering, NIT Tiruchirappalli, India.
  • Navaneethakrishna Makaram
    Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.
  • P A Karthick
    Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India; Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada. Electronic address: pakarthick1@gmail.com.
  • Ramakrishnan Swaminathan
    Indian Institute of Technology Madras, Chennai, India.

Keywords

External Resources

Popular Topics
Recent Journals