@article{koelstra2012,
	title = {{DEAP}: {A} {Database} for {Emotion} {Analysis} ;{Using} {Physiological} {Signals}},
	volume = {3},
	issn = {1949-3045},
	shorttitle = {{DEAP}},
	url = {http://ieeexplore.ieee.org/document/5871728/},
	doi = {10.1109/T-AFFC.2011.15},
	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.},
	language = {en},
	number = {1},
	urldate = {2021-07-26},
	journal = {IEEE Transactions on Affective Computing},
	author = {Koelstra, S. and Muhl, C. and Soleymani, M. and {Jong-Seok Lee} and Yazdani, A. and Ebrahimi, T. and Pun, T. and Nijholt, A. and Patras, I.},
	month = jan,
	year = {2012},
	pages = {18--31},
	file = {Koelstra et al. - 2012 - DEAP A Database for Emotion Analysis \;Using Physi.pdf:/Users/lcneuro/Zotero/storage/CYJGGSRC/Koelstra et al. - 2012 - DEAP A Database for Emotion Analysis \;Using Physi.pdf:application/pdf},
}

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