Simplicial Homology Global Optimization of EEG Signal Extraction for Emotion Recognition
Abstract
:1. Introduction
2. EEG-Based Signal Extraction for Emotion Recognition
3. Optimal EEG Signal Extraction for Emotion Recognition
3.1. EEG Topographic Maps
3.2. Objective Function
3.3. Simplicial Homology Global Optimization
4. Optimization Results and Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Ref. | Channels | Accuracy | Subject | Database | Detection |
---|---|---|---|---|---|
[3] | 14 | 77.60–78.96% | Dependent | Deap | Valence |
Arousal | |||||
Dominance | |||||
[4] | 12 | 81.5–86.87% | Independent | 12 subjects | High |
Low Valence | |||||
Arousal | |||||
[40] | 14 | 87.25% | Dependent | 19 subjects | Happiness and Sadness |
Fear and Anger | |||||
Surprise and Disgust | |||||
[41] | 32 | 96.28–96.62% | Dependent | Deap | High |
Low Valence | |||||
Arousal | |||||
[7] | 62 | 83.33% | Independent | Seed | Neutral |
Sadness and Fear | |||||
Happiness | |||||
[10] | 10 | 58.47–60.90% | Independent | N/A | High |
Low Valence | |||||
Arousal |
Layer (Type) | Output Shape | Param # |
---|---|---|
2D Convolutional | (None, 63, 63, 10) | 130 |
2D Max Pooling | (None, 31, 31, 10) | 0 |
Activation | (None, 31, 31, 10) | 0 |
2D Convolutional | (None, 30, 30, 512) | 20,992 |
2D Max Pooling | (None, 15, 15, 512) | 0 |
Activation | (None, 15, 15, 512) | 0 |
2D Convolutional | (None, 14, 14, 32) | 65,568 |
2D Max Pooling | None, (7, 7, 32) | 0 |
Activation | (None, 7, 7, 32) | 0 |
2D Convolutional | (None, 6, 6, 16) | 2064 |
2D Max Pooling | (None, 3, 3, 16) | 0 |
Activation | (None, 3, 3, 16) | 0 |
Flatten | (None, 144) | 0 |
Dense | (None, 10) | 1450 |
Dense | (None, 2) | 22 |
Total params | 90,226 | |
Trainable params | 90,226 | |
Non-trainable params | 0 |
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Roshdy, A.; Al Kork, S.; Beyrouthy, T.; Nait-ali, A. Simplicial Homology Global Optimization of EEG Signal Extraction for Emotion Recognition. Robotics 2023, 12, 99. https://doi.org/10.3390/robotics12040099
Roshdy A, Al Kork S, Beyrouthy T, Nait-ali A. Simplicial Homology Global Optimization of EEG Signal Extraction for Emotion Recognition. Robotics. 2023; 12(4):99. https://doi.org/10.3390/robotics12040099
Chicago/Turabian StyleRoshdy, Ahmed, Samer Al Kork, Taha Beyrouthy, and Amine Nait-ali. 2023. "Simplicial Homology Global Optimization of EEG Signal Extraction for Emotion Recognition" Robotics 12, no. 4: 99. https://doi.org/10.3390/robotics12040099
APA StyleRoshdy, A., Al Kork, S., Beyrouthy, T., & Nait-ali, A. (2023). Simplicial Homology Global Optimization of EEG Signal Extraction for Emotion Recognition. Robotics, 12(4), 99. https://doi.org/10.3390/robotics12040099