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Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking
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Efficiently detecting outlying behavior in video-game players.

Young Bin Kim1, Shin Jin Kang2, Sang Hyeok Lee3

  • 1Interdisciplinary Program in Visual Information Processing, Korea University , Seoul , Korea.

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|December 30, 2015
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Summary
This summary is machine-generated.

This study introduces an automated method to detect player behaviors like excitement and concentration during gameplay. The system analyzes player characteristics non-invasively, achieving a 70% recall rate for identifying specific behaviors.

Keywords:
Game environmentsOutlier detectionUser behavior analysis

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Area of Science:

  • Human-Computer Interaction
  • Behavioral Analysis
  • Machine Learning in Gaming

Background:

  • Understanding player behavior is crucial for enhancing interactive experiences.
  • Detecting specific player states (excitement, concentration, immersion, surprise) is challenging with traditional methods.
  • Non-invasive, generalizable methods for capturing player characteristics are needed.

Purpose of the Study:

  • To develop an automated method for detecting specific player behaviors during gameplay.
  • To analyze player characteristics non-invasively using multimodal data.
  • To validate the proposed method across various game genres.

Main Methods:

  • Utilized non-invasive data capture including cameras for facial expressions and movements.
  • Integrated multimodal player data: volume adjustments, keyboard, and mouse usage.
  • Employed a support vector machine (SVM) for efficient outlying behavior detection.
  • Validated the method on diverse game genres.

Main Results:

  • The proposed method successfully detected outlying player behaviors.
  • Achieved an approximate 70% recall rate for behaviors pre-identified by experts.
  • Demonstrated effectiveness across multiple game genres.

Conclusions:

  • The developed method offers an effective way to automatically detect specific player behaviors.
  • This approach provides valuable feedback for interactive content analysis in PC environments.
  • Future applications include personalized gaming experiences and adaptive game design.