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AMIGOS: A Dataset for Affect, Personality and Mood Research on Individuals and Groups

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abstract

We present AMIGOS-- A dataset for Multimodal research of affect, personality traits and mood on Individuals and GrOupS. Different to other databases, we elicited affect using both short and long videos in two social contexts, one with individual viewers and one with groups of viewers. The database allows the multimodal study of the affective responses, by means of neuro-physiological signals of individuals in relation to their personality and mood, and with respect to the social context and videos' duration. The data is collected in two experimental settings. In the first one, 40 participants watched 16 short emotional videos. In the second one, the participants watched 4 long videos, some of them alone and the rest in groups. The participants' signals, namely, Electroencephalogram (EEG), Electrocardiogram (ECG) and Galvanic Skin Response (GSR), were recorded using wearable sensors. Participants' frontal HD video and both RGB and depth full body videos were also recorded. Participants emotions have been annotated with both self-assessment of affective levels (valence, arousal, control, familiarity, liking and basic emotions) felt during the videos as well as external-assessment of levels of valence and arousal. We present a detailed correlation analysis of the different dimensions as well as baseline methods and results for single-trial classification of valence and arousal, personality traits, mood and social context. The database is made publicly available.

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representative citing papers

Detecting Fake News Belief via Skin and Blood Flow Signals

cs.HC · 2025-05-22 · conditional · novelty 5.0

A new 672-trial dataset shows that skin conductance and blood flow signals carry modest information about belief and prior exposure, with best belief classification accuracy of 67.83%.

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  • Detecting Fake News Belief via Skin and Blood Flow Signals cs.HC · 2025-05-22 · conditional · none · ref 27 · internal anchor

    A new 672-trial dataset shows that skin conductance and blood flow signals carry modest information about belief and prior exposure, with best belief classification accuracy of 67.83%.