Descriptor: Multimodal Dataset for Player Engagement Analysis in Video Games (MultiPENG)

Player engagement is crucial for understanding and optimizing gaming experiences, yet the research community lacks comprehensive multimodal datasets with reliable engagement annotations. We present a dataset combining six synchronized data streams—EEG, eye tracking, heart rate, user inputs, webcam footage, and gameplay frames—collected from 39 participants playing popular games across varying difficulty levels. Our dataset's distinctive feature lies in its temporal precision, achieved through strategic integration of engagement surveys during natural game pauses, minimizing both recall bias and gameplay disruption. The dataset includes 900 annotated gameplay sessions with four psychological metrics (engagement, interest, stress, and excitement). Initial analyses revealed surprising findings: human judges achieved only 0.48 F1-score in engagement assessment from webcam footage, while a flow theory-based model reached 0.60 F1-score using difficulty and player experience. Our multimodal neural model combining EEG, eye tracking, and facial features demonstrated the dataset's potential with a 0.51 F1-score despite class imbalance. This comprehensive dataset enables various research directions in engagement measurement and modeling, supporting the development of more robust real-time engagement detection systems. IEEE SOCIETY/COUNCIL Instrumentation and Measurement Society (IMS) DATA TYPE/LOCATION Videos, Keystrokes, Physiological Signals DATA DOI/PID 10.34740/kaggle/ds/6552328