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\begin{document}
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\title{}



\author[1]{Marios Fanourakis}%
\author[1]{Guillaume Chanel}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 06, 2026}


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\begin{abstract}
Video games are a versatile and multi-faceted stimulus which can elicit
complex player experiences. As a consequence, several datasets have been
curated or created for studying human cognition, behaviours, and
physiological responses where video games are the primary stimulus. Many
of these datasets have a low number of participants or do not have a
rich set of modalities and are always recorded in a laboratory setting.
To address these issues, we have recorded 256 participants at LAN events
while they played the first person shooter, Counter-Strike: Global
Offensive. Our dataset consists of several complementary modalities:
physiological signals (ECG, EDA, Respiration), behavioural signals
(facial expressions, eyetracking, depth images, seat pressure), computer
interaction (keyboard and mouse events, game actions), and stimulus
information (gameplay video, game logs). We show that the number of
participants in our dataset and the variety of modalities recorded is
advantageous for training machine learning models.%
\end{abstract}%



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