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Multiple-output support vector machine regression with feature selection for arousal/valence space emotion

Cristian A Torres-Valencia, Mauricio A Álvarez, Alvaro A Orozco-Gutiérrez

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 9, 2015
    PubMed
    Summary

    Simultaneously modeling arousal and valence dimensions improves human emotion recognition. This multimodal approach, using Electroencephalogram and physiological signals, effectively identifies key emotional indicators.

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    Area of Science:

    • Affective computing
    • Computational neuroscience
    • Human-computer interaction

    Background:

    • Human emotion recognition (HER) traditionally used discrete emotion models.
    • Dimensional emotion spaces, like Arousal/Valence, offer broader affective state characterization.
    • Existing HER systems typically model these dimensions independently.

    Purpose of the Study:

    • To investigate the impact of simultaneously modeling arousal and valence dimensions in HER.
    • To demonstrate the advantages of a joint modeling approach over independent modeling.
    • To identify informative physiological and neural signals for emotion recognition.

    Main Methods:

    • Utilized a multimodal approach incorporating Electroencephalogram (EEG) and physiological signals (e.g., EOG/EMG, GSR).
    • Employed a multiple-output Support Vector Machine (SVM) regressor for joint dimension modeling.
    • Implemented Recursive Feature Elimination (RFE) for embedded feature selection within the SVM framework.

    Main Results:

    • Simultaneous modeling of arousal and valence dimensions was experimentally validated.
    • Recursive Feature Elimination (RFE) successfully reduced feature subsets without performance degradation.
    • EEG, Electrooculogram/Electromyogram (EOG/EMG), and Galvanic Skin Response (GSR) were identified as highly informative signals.

    Conclusions:

    • Simultaneous modeling enhances the performance of HER systems in dimensional emotion spaces.
    • Feature selection via RFE is effective for optimizing multimodal HER models.
    • Specific physiological and neural signals provide crucial information for recognizing arousal and valence.