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Emotion classification during music listening from forehead biosignals

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Abstract

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 %.

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Acknowledgments

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

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Authors and Affiliations

  1. Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Mohsen Naji

  2. Department of Medical Physics, Tarbiat Modares University, Tehran, Iran

    Mohammd Firoozabadi

  3. Department of Psychology, Tarbiat Modares University, Tehran, Iran

    Parviz Azadfallah

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  1. Mohsen Naji
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  2. Mohammd Firoozabadi
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  3. Parviz Azadfallah
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Correspondence to Mohsen Naji.

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Naji, M., Firoozabadi, M. & Azadfallah, P. Emotion classification during music listening from forehead biosignals. SIViP 9, 1365–1375 (2015). https://doi.org/10.1007/s11760-013-0591-6

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  • DOI: https://doi.org/10.1007/s11760-013-0591-6

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