DEAP: A Database for Emotion Analysis Using Physiological Signals

Sander Koelstra, C. Mühl, Mohammad Soleymani, Jung Seok Lee, Ashkan Yazdani, Touradj Ebrahimi, Thierry Pun, Antinus Nijholt, Ioannis Patras

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    Abstract

    We present a multimodal dataset for the analysis of human affective states. The electroencephalogram (EEG) and peripheral physiological signals of 32 participants were recorded as each watched 40 one-minute long excerpts of music videos. Participants rated each video in terms of the levels of arousal, valence, like/dislike, dominance and familiarity. For 22 of the 32 participants, frontal face video was also recorded. A novel method for stimuli selection is proposed using retrieval by affective tags from the last.fm website, video highlight detection and an online assessment tool. An extensive analysis of the participants' ratings during the experiment is presented. Correlates between the EEG signal frequencies and the participants' ratings are investigated. Methods and results are presented for single-trial classification of arousal, valence and like/dislike ratings using the modalities of EEG, peripheral physiological signals and multimedia content analysis. Finally, decision fusion of the classification results from the different modalities is performed. The dataset is made publicly available and we encourage other researchers to use it for testing their own affective state estimation methods.
    Original languageUndefined
    Pages (from-to)18-31
    Number of pages15
    JournalIEEE transactions on affective computing
    Volume3
    Issue number1
    DOIs
    Publication statusPublished - 2012

    Keywords

    • Affect sensing and analysis
    • Machine Learning
    • Multimedia Retrieval
    • Music retrieval and generation
    • Methods for emotion elicitation
    • IR-79512
    • Emotion in human-computer interaction
    • Emotional Corpora
    • Physiological Measures
    • EWI-21368
    • METIS-285051
    • Methods of data collection

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