| Acc | Accuracy |
| AEE | Addditive Expert Ensemble |
| ANN | Artificial Neural Network |
| ARF | Adaptive Random Forest |
| CEM | Categorical Emotion Model |
| CNS | Central Nervous System |
| Db4 | Wavelet Daubechies 4 |
| DEAP | Database for Emotion Analysis using Physiological Signals |
| DL | Deep Learning |
| DS | Data stream |
| DT | Decision Tree |
| DEM | Dimensional Emotion Model |
| DWE | Dynamic Weighted Ensemble |
| EEG | electroencephalogram |
| FN | False Negative |
| FP | False Positive |
| GD | Gradient descent |
| HT | Hoeffding Tree |
| HAT | Hoeffding Adaptive Tree |
| KNN | K-Nearest Neighbors |
| LMS | Learning Management Systems |
| LR | Logistic Regression |
| ML | Machine learning |
| MLP | Multi-layer Perceptron |
| MOA | Massive Online Analysis |
| MOOC | Massive Online Open Courses |
| mr | Misclassification Rate |
| Pre | Precision |
| Rec | Recall |
| RECS | Real-time Emotion Classification System |
| SGD | Stochastic gradient descent |
| SSE | Sum of squared error |
| STD | Standard deviation |
| SVM | Support Vector Machine |
| TP | True Positive |
| TN | True Negative |
| VA | Valence-arousal |
| VAD | Valence-arousal-dominance |
| WT | Wavelet Transformation |