Affective gaming has been an active research field recently. This is due to the importance of the player’s emotions while playing computer games. Emotions can be detected from various modalities such as facial, voice, and physiological signals. In this study, we evaluate an XGBoost ensemble method and deep neural network for detecting naturalistic expressions of emotions of video game players using physiological signals. Physiological data was collected from twelve participants while playing PUBG mobile game. Both Discrete and dimensional emotion models were evaluated. We evaluated the performance of classification models using individual physiological channels and a fusion of these channels. A comparison between user-dependent, and user-independent is also provided. Our results indicated that the use of the dimensional valence and arousal model can provide more accurate accuracy than the discrete emotion model. The results also showed that ECG features and a fusion of features from all physiological channels provide the highest affect detection accuracy. Our deep neural network model based on user-dependent model achieved the highest accuracy with 77.92% and 78.58% of detecting valence, and arousal respectively using a fusion of features. The user-independent models were not feasible, presumably due to strong individual differences of physiological responses.

情感游戏最近一直是一个活跃的研究领域。这是因为玩家在玩电脑游戏时情绪的重要性。可以从各种形式（例如面部、语音和生理信号）检测情绪。在这项研究中，我们评估了 XGBoost 集成方法和深度神经网络，用于使用生理信号检测视频游戏玩家情绪的自然表达。生理数据是在玩 PUBG 手机游戏时从 12 名参与者那里收集的。评估了离散和维度情感模型。我们使用单个生理通道和这些通道的融合来评估分类模型的性能。还提供了用户相关和用户无关之间的比较。我们的结果表明，与离散情绪模型相比，使用维度效价和唤醒模型可以提供更准确的准确性。结果还表明，心电图特征和来自所有生理通道的特征融合提供了最高的影响检测精度。我们基于用户依赖模型的深度神经网络模型使用特征融合实现了最高的准确率，检测效价和唤醒率分别为 77.92% 和 78.58%。独立于用户的模型不可行，大概是由于生理反应的强烈个体差异。我们基于用户依赖模型的深度神经网络模型使用特征融合实现了最高的准确率，检测效价和唤醒率分别为 77.92% 和 78.58%。独立于用户的模型不可行，大概是由于生理反应的强烈个体差异。我们基于用户依赖模型的深度神经网络模型使用特征融合实现了最高的准确率，检测效价和唤醒率分别为 77.92% 和 78.58%。独立于用户的模型是不可行的，大概是由于生理反应的强烈个体差异。