Department of Computer Science, Universitat Politècnica de Catalunya (BarcelonaTech), 08034 Barcelona, Spain.
Eurecat, Centre Tecnològic de Catalunya, 08005 Barcelona, Spain.
Sensors (Basel). 2021 Feb 25;21(5):1589. doi: 10.3390/s21051589.
In face-to-face and online learning, emotions and emotional intelligence have an influence and play an essential role. Learners' emotions are crucial for e-learning system because they promote or restrain the learning. Many researchers have investigated the impacts of emotions in enhancing and maximizing e-learning outcomes. Several machine learning and deep learning approaches have also been proposed to achieve this goal. All such approaches are suitable for an offline mode, where the data for emotion classification are stored and can be accessed infinitely. However, these offline mode approaches are inappropriate for real-time emotion classification when the data are coming in a continuous stream and data can be seen to the model at once only. We also need real-time responses according to the emotional state. For this, we propose a real-time emotion classification system (RECS)-based Logistic Regression (LR) trained in an online fashion using the Stochastic Gradient Descent (SGD) algorithm. The proposed RECS is capable of classifying emotions in real-time by training the model in an online fashion using an EEG signal stream. To validate the performance of RECS, we have used the DEAP data set, which is the most widely used benchmark data set for emotion classification. The results show that the proposed approach can effectively classify emotions in real-time from the EEG data stream, which achieved a better accuracy and than other offline and online approaches. The developed real-time emotion classification system is analyzed in an e-learning context scenario.
在面对面和在线学习中，情感和情绪智力都有影响，并发挥着重要作用。学习者的情绪对于电子学习系统至关重要，因为它们促进或抑制学习。许多研究人员已经研究了情感在增强和最大化电子学习成果方面的影响。也已经提出了几种机器学习和深度学习方法来实现这一目标。所有这些方法都适用于离线模式，在离线模式下，情感分类的数据被存储并可以无限访问。然而，当数据以连续流的形式出现，并且模型只能一次看到数据时，这些离线模式的方法不适合实时情感分类。我们还需要根据情绪状态做出实时响应。为此，我们提出了一种基于实时情感分类系统（RECS）的逻辑回归（LR），该系统使用随机梯度下降（SGD）算法在线训练。该提议的 RECS 能够通过使用 EEG 信号流在线训练模型实时分类情感。为了验证 RECS 的性能，我们使用了最广泛用于情感分类的基准数据集 DEAP 数据集。结果表明，该方法可以有效地从 EEG 数据流中实时分类情感，其准确性和优于其他离线和在线方法。开发的实时情感分类系统在电子学习情境场景中进行了分析。
