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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

Summary

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.