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Real-Time Emotion Classification Using EEG Data Stream in E-Learning Contexts - PubMed

  • ️Fri Jan 01 2021

Real-Time Emotion Classification Using EEG Data Stream in E-Learning Contexts

Arijit Nandi et al. Sensors (Basel). 2021.

Abstract

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.

Keywords: e-learning; emotion classification; logistic regression; online training; real-time emotion classification; stochastic gradient descent.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1

Online emotion classification from a data stream.

Figure 2
Figure 2

The Russel 2D valence–arousal (VA) space emotion model.

Figure 3
Figure 3

The framework of our proposed work.

Figure 4
Figure 4

Learning rate vs model error.

Figure 5
Figure 5

Valence classification comparison among different online classifiers. In (a) shows the accuracy and (b) shows the F1-score comparison.

Figure 6
Figure 6

Arousal classification comparison among different online classifiers. In (a) is shown the accuracy and (b) shows the F1-score comparison.

Figure 7
Figure 7

Confusion matrix comparison among different online classifiers for valence classification with our proposed RECS.

Figure 8
Figure 8

Confusion matrix comparison among different online classifiers for arousal classification with our proposed RECS.

Figure 9
Figure 9

New generation of EEG head bands.

Figure 10
Figure 10

An application scenario.

Figure 11
Figure 11

Confusion matrix for discrete emotion mapping based on valence–arousal status.

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