AMIGOS: A Dataset for Affect, Personality and Mood Research on Individuals and Groups
By Juan Abdon Miranda Correa, Mojtaba Khomami Abadi, Nicu Sebe, Ioannis Patras. IEEE Transactions on Affective Computing, 2017
608 citations recorded by the source corpus.
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.
Subfields: Cardiology and Cardiovascular Medicine, Experimental and Cognitive Psychology
View Paper: https://www.semanticscholar.org/paper/e53b6ca45ddae2c97a5545fb163d2caacd7a417d
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In Cardiology and Cardiovascular Medicine
In Experimental and Cognitive Psychology
Citations in the corpus
- TSception: Capturing Temporal Dynamics and Spatial Asymmetry From EEG for Emotion Recognition (2021, methodology): “To evaluate the proposed TSception, we conduct emotion classification experiments on two publicly available benchmark datasets, a Database for Emotion Analysis using Physiological signals (DEAP) [31], and A Dataset for Affect, Personality and Mood Research on Individuals and Groups (AMIGOS) [32].”
- Self-Supervised ECG Representation Learning for Emotion Recognition (2020, methodology): “We use four publicly available datasets, AMIGOS [5], DREAMER [23], WESAD [24], and SWELL [10] to perform emotion recognition with ECG.”