Signal, Image and Video Processing

, Volume 9, Issue 6, pp 1365–1375 | Cite as

Emotion classification during music listening from forehead biosignals

  • Mohsen Naji
  • Mohammd Firoozabadi
  • Parviz Azadfallah
Original Paper


Emotion recognition systems are helpful in human–machine interactions and clinical applications. This paper investigates the feasibility of using 3-channel forehead biosignals (left temporalis, frontalis, and right temporalis channel) as informative channels for emotion recognition during music listening. Classification of four emotional states (positive valence/low arousal, positive valence/high arousal, negative valence/high arousal, and negative valence/low arousal) in arousal–valence space was performed by employing two parallel cascade-forward neural networks as arousal and valence classifiers. The inputs of the classifiers were obtained by applying a fuzzy rough model feature evaluation criterion and sequential forward floating selection algorithm. An averaged classification accuracy of 87.05 % was achieved, corresponding to average valence classification accuracy of 93.66 % and average arousal classification accuracy of 93.29 %.


Forehead biosignals Arousal Valence Emotion recognition 



We gratefully acknowledge the assistance of Ms Atena Bajoulvand for her help with collection of the data of female subjects.


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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Mohsen Naji
    • 1
  • Mohammd Firoozabadi
    • 2
  • Parviz Azadfallah
    • 3
  1. 1.Department of Biomedical Engineering, Science and Research BranchIslamic Azad UniversityTehranIran
  2. 2.Department of Medical PhysicsTarbiat Modares UniversityTehranIran
  3. 3.Department of PsychologyTarbiat Modares UniversityTehranIran

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