Analysis of Multiple Emotions from Electroencephalogram Signals Using Machine Learning Models †
Abstract
1. Introduction
- Develop a suitable VAD model to categorize 16 emotions which is high when compared to the existing state-of-the-art techniques.
- Evaluate the performance of the machine learning model for 2-class, 4-class, and 16-class and hence, identify a suitable machine learning model for multiple class classification of emotion.
2. Methodology
3. Result and Discussion
3.1. SVM
3.2. LDA
3.3. KNN
3.4. Decision Tree
3.5. Naive Bayes
3.6. Random Forest
3.7. Statistical Analysis
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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No. of Class | Categories |
---|---|
2—(V) (A) | Valence(V), Arousal(A) |
4—(VA) | High Arousal High Valence (HAHV), High Arousal Low Valence (HALV), Low Arousal High Valence (LAHV), and Low Arousal Low Valence (LALV) |
16—(VAD) | Sadness, Shame, Guilt, Envy, Satisfaction, Relief, Hope, Interest, Fear, Disgust, Contempt, Anger, Pride, Elation, Joy, and Surprise |
Name/Description | Version |
---|---|
CPU | Intel® Core™ i5 |
RAM | 8 GB |
OS | Windows 10 |
Python | Python 3.11.5 |
TensorFlow | TensorFlow 2.14.0 |
Scikit-learn | Scikit-learn 1.3.1 |
Anaconda | 2021.05 |
Model | SVM-Linear | |||
---|---|---|---|---|
Accuracy | Precision | Recall | F1-Score | |
2- Class | 73.81% | 72% | 64% | 65% |
4-Class | 48.26% | 46% | 42% | 42% |
16-Class | 37.01% | 35% | 37% | 35% |
SVM-RBF | ||||
2- Class | 67.35% | 34% | 50% | 40% |
4-Class | 38.75% | 10% | 25% | 14% |
16-Class | 24.4% | 2% | 7% | 3% |
Model | LDA | |||
---|---|---|---|---|
Accuracy | Precision | Recall | F1-Score | |
2-Class | 73.69% | 70% | 66% | 67% |
4-Class | 49.97% | 49% | 43% | 44% |
16-Class | 33.86% | 28% | 29% | 25% |
Model | KNN | |||
---|---|---|---|---|
Accuracy | Precision | Recall | F1-Score | |
2-Class | 95.81% | 95% | 95% | 95% |
4-Class | 91.78% | 92% | 92% | 92% |
16-Class | 89.26% | 89% | 90% | 89% |
Model | Decision Tree | |||||||
---|---|---|---|---|---|---|---|---|
Accuracy | Precision | Recall | F1-Score | |||||
Without Entropy | With Entropy | Without Entropy | With Entropy | Without Entropy | With Entropy | Without Entropy | With Entropy | |
2-Class | 86.71% | 87.56% | 85% | 86% | 85% | 86% | 85% | 86% |
4-Class | 76.39% | 77.05% | 75% | 76% | 76% | 77% | 76% | 77% |
16-Class | 69.68% | 70.08% | 67% | 68% | 68% | 68% | 67% | 68% |
Model | Naive Bayes | |||
---|---|---|---|---|
Accuracy | Precision | Recall | F1-Score | |
2- Class | 58.46% | 59% | 60% | 57% |
4-Class | 38.29% | 29% | 28% | 24% |
16-Class | 7.55% | 18% | 26% | 7% |
Model | Random Forest | |||
---|---|---|---|---|
Accuracy | Precision | Recall | F1-Score | |
2- Class | 93.20% | 93% | 91% | 92% |
4-Class | 87.59% | 89% | 87% | 87% |
16-Class | 84.70% | 87% | 82% | 84% |
Statistical Analysis for 2-class, 4-class, and 16-class | |
2-class | |
Friedman Test Statistic | 19.60431 |
p-value | 0.001482 |
4-class | |
Friedman Test Statistic | 19.49275 |
p-value | 0.001555 |
16-class | |
Friedman Test Statistic | 20.0 |
p-value | 0.001249 |
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Share and Cite
Margaret Matthew, J.; Banu Noordheen Mohammad Mustafa, M.; Selvarajan, M. Analysis of Multiple Emotions from Electroencephalogram Signals Using Machine Learning Models. Eng. Proc. 2024, 82, 41. https://doi.org/10.3390/ecsa-11-20398
Margaret Matthew J, Banu Noordheen Mohammad Mustafa M, Selvarajan M. Analysis of Multiple Emotions from Electroencephalogram Signals Using Machine Learning Models. Engineering Proceedings. 2024; 82(1):41. https://doi.org/10.3390/ecsa-11-20398
Chicago/Turabian StyleMargaret Matthew, Jehosheba, Masoodhu Banu Noordheen Mohammad Mustafa, and Madhumithaa Selvarajan. 2024. "Analysis of Multiple Emotions from Electroencephalogram Signals Using Machine Learning Models" Engineering Proceedings 82, no. 1: 41. https://doi.org/10.3390/ecsa-11-20398
APA StyleMargaret Matthew, J., Banu Noordheen Mohammad Mustafa, M., & Selvarajan, M. (2024). Analysis of Multiple Emotions from Electroencephalogram Signals Using Machine Learning Models. Engineering Proceedings, 82(1), 41. https://doi.org/10.3390/ecsa-11-20398