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Boredom, engagement and anxiety as indicators for adaptation to difficulty in games

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References

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Cited by

2011A review of the use of psychophysiological methods in game research288 cited
2014Dynamic difficulty using brain metrics of workload148 cited
2012Valence, arousal and dominance in the EEG during game play146 cited
2009Emotion recognition system using brain and peripheral signals: Using correlation dimension to improve the results of EEG145 cited
2015Understanding the psychophysiology of flow: A driving simulator experiment to investigate the relationship between flow and heart rate variability129 cited
2013Boredom and Distraction in Multiple Unmanned Vehicle Supervisory Control109 cited
2010Electroencephalographic Assessment of Player Experience

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106 cited

2020Peripheral-physiological and neural correlates of the flow experience while playing video games: a comprehensive review96 cited

2016Adaptation in Affective Video Games: A Literature Review80 cited

2018Task Engagement as Personalization Feedback for Socially-Assistive Robots and Cognitive Training80 cited

2009Affective Pacman: A Frustrating Game for Brain-Computer Interface Experiments74 cited

2011Automatic Recognition of Boredom in Video Games Using Novel Biosignal Moment-Based Features70 cited

2018Detecting boredom from eye gaze and EEG65 cited

2019DeepFlow: Detecting Optimal User Experience From Physiological Data Using Deep Neural Networks63 cited

2012Capturing user engagement via psychophysiology: measures and mechanisms for biocybernetic adaptation60 cited

2014Detection of Human Emotions Using Features Based on the Multiwavelet Transform of EEG Signals55 cited