Exploiting Real-Time EEG Analysis for Assessing Flow in Games
Exploiting Real-Time EEG Analysis for Assessing Flow in Games
复制标题
利用实时脑电图分析来评估游戏中的流程
DOI：
10.1109/icalt.2012.144
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Flavio Ansovini
中科院分区：
文献类型：
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作者：
A. Plotnikov;N. Stakheika;A. D. Gloria;Carlotta Schatten;F. Bellotti;Riccardo Berta;C. Fiorini;Flavio Ansovini
适应不同用户的能力被认为是将严肃游戏的影响力和有效性最大化的关键。神经科学的进步使得持续监控玩家状态成为可能。本文报道了一种简单的（4电极）商用脑电图（EEG）监测玩家流状态的研究工作。我们特别关注三个重要的研究问题：是否有可能从统计上区分心流和无聊状态？哪个波长？是否可以识别不同程度的无聊感和心流？结果——即使由于测试规模小而受到限制——是有希望的，并使进一步的研究成为可能。在不同的条件下，脑电波可以观察到统计学上的显著差异。机器学习分类器（SVM）在两级区分案例中取得了成功，特别是在个性化训练中。
Ability of adapting to different users is considered key to maximize the impact and effectiveness of Serious Games (SGs). Advances in neurosciences are making it possible to continuously monitor the player status. This paper reports the research work on the monitoring of the player flow status with a simple (4 electrode) commercial electroencephalogram (EEG). In particular, we focus on three consequential research questions: is it possible to statistically distinguish a flow from a boredom condition? For which wavelengths? Can different levels of boredom and flow be identified? Results - even if limited because of the small size of the test, are promising and enable further research. Statistically significant differences could be observed for brainwaves under various conditions. A machine learning classifier (SVM) was successful in the 2-level distinction case, in particular with personalized training.