ArticleScientific data2022
BIRAFFE2, a multimodal dataset for emotion-based personalization in rich affective game environments.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Achieving Gameplay Independence in Virtual Reality Exergames for Individuals With Mild Intellectual Disabilities: Pilot Study.JMIR serious games · 2025Article
- AMuCS: Affective multimodal Counter-Strike video game dataset.Scientific data · 2025Article
- The competitive esports physiological, affective, and video dataset.Scientific data · 2025Article
- GameVibe: a multimodal affective game corpus.Scientific data · 2024Article
- A real-world dataset of group emotion experiences based on physiological data.Scientific data · 2024Article
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6 authors.
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Abstract
Generic emotion prediction models based on physiological data developed in the field of affective computing apparently are not robust enough. To improve their effectiveness, one needs to personalize them to specific individuals and incorporate broader contextual information. To address the lack of relevant datasets, we propose the 2nd Study in Bio-Reactions and Faces for Emotion-based Personalization for AI Systems (BIRAFFE2) dataset. In addition to the classical procedure in the stimulus-appraisal paradigm, it also contains data from an affective gaming session in which a range of contextual data was collected from the game environment. This is complemented by accelerometer, ECG and EDA signals, participants' facial expression data, together with personality and game engagement questionnaires. The dataset was collected on 102 participants. Its potential usefulness is presented by validating the correctness of the contextual data and indicating the relationships between personality and participants' emotions and between personality and physiological signals.
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