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Related Experiment Video

Updated: Jun 6, 2025

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking
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GameVibe: a multimodal affective game corpus.

Matthew Barthet1, Maria Kaselimi2, Kosmas Pinitas2

  • 1Institute of Digital Games, University of Malta, Msida, Malta. matthew.barthet@um.edu.mt.

Scientific Data
|November 29, 2024
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Summary
This summary is machine-generated.

Researchers introduce GameVibe, a new multimodal affect corpus featuring diverse gameplay videos and viewer engagement data. This dataset addresses the need for high-quality audiovisual stimuli in affective computing research.

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Area of Science:

  • Affective computing
  • Multimodal machine learning
  • Human-computer interaction

Background:

  • Growing online video platforms necessitate complex, multimodal affective computing research.
  • Existing datasets often lack high-quality audiovisual stimuli for viewer engagement studies.

Purpose of the Study:

  • Introduce GameVibe, a novel affect corpus.
  • Provide diverse, high-quality multimodal audiovisual stimuli for affective computing research.
  • Facilitate studies on viewer engagement using gameplay data.

Main Methods:

  • Compiled a corpus of videos from 30 diverse, publicly available gameplay sessions.
  • Included in-game behavioral observations and third-person affect traces.
  • Ensured high-quality audiovisual and gameplay diversity in stimuli.

Main Results:

  • GameVibe offers a rich collection of multimodal audiovisual stimuli.
  • The corpus enables nuanced analysis of viewer engagement.
  • Analysis confirmed reliable annotator agreement for affect traces.

Conclusions:

  • GameVibe addresses the critical need for diverse, high-quality affect corpora.
  • The dataset supports advanced research in multimodal affective computing.
  • This resource facilitates deeper understanding of viewer engagement in gaming contexts.