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DEAP : a Database for Emotion Analysis Using Physiological Signals

Koelstra, Sander
Mühl, Christian
Lee, Jong-Seok
Ebrahimi, Touradj
Nijholt, Anton
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Published in IEEE Transactions on Affective Computing. 2012, vol. 3, no. 1, p. 18-31
Abstract We present a multimodal data set 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 different modalities is performed. The data set is made publicly available and we encourage other researchers to use it for testing their own affective state estimation methods.
Keywords Web sitesElectroencephalographyEmotion recognitionImage classificationInformation retrievalMultimedia computingNeurophysiologyState estimationVideo signal processingDEAPEEG signal frequenciesWeb siteArousalDecision fusionDominanceElectroencephalogramEmotion analysisFamiliarityFrontal face videoHuman affective statesMultimedia content analysisMultimodal data setMusic videosOnline assessment toolPeripheral physiological signalsSingle-trial classificationState estimation methodsStimuli selectionVideo highlight detectionDatabasesFaceMotion picturesMultimedia communicationVideosVisualizationEEGEmotion classificationAffective computing.Pattern classificationPhysiological signalsSignal processing
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Research groups Affective sciences
Computer Vision and Multimedia Laboratory
Multimodal Interaction Group
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KOELSTRA, Sander et al. DEAP : a Database for Emotion Analysis Using Physiological Signals. In: IEEE Transactions on Affective Computing, 2012, vol. 3, n° 1, p. 18-31. doi: 10.1109/T-AFFC.2011.15 https://archive-ouverte.unige.ch/unige:47405

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Deposited on : 2015-03-03

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