Статья

DEAP: A Database for Emotion Analysis ;Using Physiological Signals

Sander KoelstraSchool of Electronic Engineering and Computer Science, Queen Mary, University of London, London, UKChristian MühlHuman Media Interaction Group, University of Twente, Enschede, NetherlandsMohammad SoleymaniComputer Science Department, University of Geneva, Geneva, SwitzerlandJong‐Seok LeeSchool of Integrated Technology, Yonsei University, South KoreaAmirmehdi YazdaniMultimedia Signal Processing Group, Institute of Electrical Engineering (IEL), Ecole Polytechnique Fédéral de Lausanne, Lausanne, SwitzerlandTouradj EbrahimiMultimedia Signal Processing Group, Institute of Electrical Engineering (IEL), Ecole Polytechnique Fédéral de Lausanne, Lausanne, SwitzerlandThierry PunComputer Science Department, University of Geneva, Geneva, SwitzerlandAnton NijholtHuman Media Interaction Group, University of Twente, Enschede, NetherlandsIoannis PatrasSchool of Electronic Engineering and Computer Science, Queen Mary, University of London, London, UK

2011en

ABI

Аннотация

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.

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Идентификаторы

DOI: 10.1109/t-affc.2011.15

Цитирования и источники

Цитирований: 3Использованных источников: 0

Показатели — AkademScholar