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 F1-score than other offline and online approaches. The developed real-time emotion classification system is analyzed in an e-learning context scenario.

中文翻译：

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在电子学习环境中使用EEG数据流进行实时情感分类

在面对面和在线学习中，情绪和情商具有影响力，并起着至关重要的作用。学习者的情绪对电子学习系统至关重要，因为它们可以促进或限制学习。许多研究人员已经研究了情绪对增强和最大化电子学习成果的影响。还提出了几种机器学习和深度学习方法来实现此目标。所有这些方法都适用于离线模式，在该模式下，用于情感分类的数据已存储并且可以无限访问。但是，当数据以连续流的形式进入并且模型一次只能看到数据时，这些离线模式方法不适用于实时情绪分类。我们还需要根据情绪状态进行实时响应。为了这，我们提出使用随机梯度下降（SGD）算法以在线方式训练的基于实时情绪分类系统（RECS）的Logistic回归（LR）。通过使用EEG信号流以在线方式训练模型，提出的RECS能够实时对情绪进行分类。为了验证RECS的性能，我们使用了DEAP数据集，它是用于情感分类的最广泛使用的基准数据集。结果表明，该方法可以有效地从脑电数据流中实时对情绪进行分类，取得了较好的准确性和准确性。通过使用EEG信号流以在线方式训练模型，提出的RECS能够实时对情绪进行分类。为了验证RECS的性能，我们使用了DEAP数据集，它是用于情感分类的最广泛使用的基准数据集。结果表明，该方法可以有效地从脑电数据流中实时对情绪进行分类，取得了较好的准确性和准确性。通过使用EEG信号流以在线方式训练模型，提出的RECS能够实时对情绪进行分类。为了验证RECS的性能，我们使用了DEAP数据集，它是用于情感分类的最广泛使用的基准数据集。结果表明，该方法可以有效地从脑电数据流中实时对情绪进行分类，取得了较好的准确性和准确性。F1得分高于其他离线和在线方法。在电子学习环境中分析了开发的实时情绪分类系统。