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AMuCS: Affective multimodal Counter-Strike video game dataset.

Marios Fanourakis¹, Guillaume Chanel²

¹Social Intelligence and MultiSensing (SIMS) lab, University of Geneva, Geneva, Switzerland. marios.fanourakis@unige.ch.

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|July 30, 2025

Summary

This study introduces a large dataset of 256 players of Counter-Strike: Global Offensive, collected during live events. The rich, multi-modal data is ideal for training advanced machine learning models in human-computer interaction research.

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Area of Science:

Human-Computer Interaction
Cognitive Science
Machine Learning

Background:

Video games offer complex stimuli for studying human cognition and behavior.
Existing datasets often lack participant numbers, data modalities, or are confined to lab settings.

Purpose of the Study:

To address limitations of prior datasets by creating a large-scale, multi-modal dataset.
To facilitate advanced machine learning model training for analyzing player experiences.

Main Methods:

Recorded 256 participants playing Counter-Strike: Global Offensive at LAN events.
Collected diverse data: physiological (ECG, EDA, Respiration), behavioral (facial expressions, eye-tracking, depth, pressure), and interaction (keyboard/mouse, game actions).
Included stimulus information: gameplay video and game logs.

Main Results:

The dataset features a high number of participants (256) and a wide array of complementary data modalities.
Demonstrated the dataset's advantage for training machine learning models.

Conclusions:

The large-scale, multi-modal dataset collected at live events is a valuable resource.
This dataset enables more robust machine learning applications for understanding player behavior and cognition in gaming.