Optimizing Hyperparameters of CNN and DNN for Emotion Classification Based on EEG Signals

Authors

  • Dian Palupi Rini Universitas Sriwijaya
  • Winda Kurnia Sari Faculty of Computer Science, Universitas Sriwijaya, Indonesia

DOI:

https://doi.org/10.21108/ijoict.v10i1.857

Keywords:

EEG Emotion, Optimizing, Hyperparameter, CNN, DNN

Abstract

EEG emotion is a research topic that has gained significant attention in the development of emotion classification systems. This study focuses on optimizing the hyperparameters of CNN (Convolutional Neural Network) and DNN (Deep Neural Network) for classifying EEG emotion signals. The data is divided into three train-test data ratio scenarios: 80:20, 70:30, and 60:40. After modeling and the classification process, hyperparameter tuning was conducted on both models to achieve the best results. Experimental results showed the highest accuracy of 98.36% for CNN, while DNN reached 98.18% in the 80:20 data ratio scenario. Despite the high accuracy, the differences in the loss curves between CNN and DNN reflect the complexity of the performance of both models. The train-test data ratio was also found to significantly impact the performance of both models, with the 80:20 data split yielding the best results, while the 70:30 and 60:40 splits resulted in slightly lower accuracies.

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References

[1] G. Zhao, Y. Ge, B. Shen, X. Wei, and H. Wang, “Emotion Analysis for Personality Inference from EEG Signals,” IEEE Trans. Affect. Comput., vol. 9, no. 3, pp. 362–371, 2018.
[2] J. J. Bird, A. Ekart, C. D. Buckingham, and D. R. Faria, “Mental emotional sentiment classification with an eeg-based brain-machine interface.” The International Conference on Digital Image and Signal Processing (DISP’19), Springer, 2019.
[3] R. Mohakud and R. Dash, “Designing a grey wolf optimization based hyper-parameter optimized convolutional neural network classifier for skin cancer detection.” 2021.
[4] W. X. Cheng, R. Gao, P. N. Suganthan, and K. F. Yuen, “EEG-based emotion recognition using random Convolutional Neural Networks,” Eng. Appl. Artif. Intell., vol. 116, p. 105349, 2022.
[5] Y. Yin, X. Zheng, B. Hu, Y. Zhang, and X. Cui, “EEG Emotion Recognition using Fusion Model of Graph Convolutional Neural Network and LSTM.” Applied Soft Computing Journal, 2020.
[6] X. Du et al., “An Efficient LSTM Network for Emotion Recognition from Multichannel EEG Signals.” IEEE, 2020.
[7] J. X. Chen, D. M. Jiang, and Y. N. Zhang, “A Hierarchical Bidirectional GRU Model With Attention for EEG-Based Emotion Classification,” IEEE Access, vol. 7, pp. 118530–118540, 2019.
[8] Y. Zhang et al., “An Investigation of Deep Learning Models for EEG-based Emotion Recognition.” Front. Neurosci. 14:1344., 2020.
[9] J. Wang and M. Wang, “Review of the emotional feature extraction and classification using EEG signals,” Cogn. Robot., vol. 1, pp. 29–40, 2021.
[10] T. S, D. K. M. R. Kumar, and J. L. A. Nataraj, “Analysis of EEG Based Emotion Detection of DEAP and SEED-IV Databases Using SVM.” Proceedings of the Second International Conference on Emerging Trends in Science & Technologies For Engineering Systems (ICETSE-2019), 2019.
[11] S. Joshi and F. Joshi, “Human Emotion Classification Based on EEG Signals Using Recurrent Neural Network and KNN.” arXiv preprint arXiv:2205.08419, 2022.
[12] B. Chakravarthi, S.-C. Ng, M. R. Ezilarasan, and M.-F. Leung, “EEG-based emotion recognition using hybrid CNN and LSTM classification,” Front. Comput. Neurosci., vol. 16, 2022.
[13] S. T. Aung et al., “Entropy-Based Emotion Recognition from Multichannel EEG Signals Using Artificial Neural Network,” Comput. Intell. Neurosci., vol. 2022, p. 6000989, 2022.
[14] J. J. Bird, L. J. Manso, E. P. Ribiero, A. Ekart, and D. R. Faria, “A study on mental state classification using eeg-based brain-machine interface.” 9th International Conference on Intelligent Systems, IEEE, 2018.
[15] J. Lanchantin, T. Wang, V. Ordonez, and Y. Qi, “General Multi-label Image Classification with Transformers.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16478–16488, 2021.
[16] L. Yang and A. Shami, “On hyperparameter optimization of machine learning algorithms: Theory and practice,” Neurocomputing, vol. 415, pp. 295–316, 2020.

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Published

2024-06-24

How to Cite

Rini, D. P., & Kurnia Sari, W. . (2024). Optimizing Hyperparameters of CNN and DNN for Emotion Classification Based on EEG Signals. International Journal on Information and Communication Technology (IJoICT), 10(1), 1–12. https://doi.org/10.21108/ijoict.v10i1.857

Issue

Vol. 10 No. 1 (2024): Vol. 10 No.1 June 2024

Section

Data Science

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