i-manager Publications

RNN LSTM-Based Emotion Recognition using EEG Signals

Akalya Devi C.*, Karthika Renuka D.**, Abishek L. R.***, Kaaviya A.****, Prediksha R. L.*****, Yaswanth M.******

*-****** Department of Information Technology, PSG College of Technology, Coimbatore, Tamil Nadu, India.

Periodicity:January - June'2023
DOI : https://doi.org/10.26634/jaim.1.1.19112

Abstract

Emotion, a representation of the human state of mind, plays an important role in day-to-day human life and helps one make good decisions. A typical way to understand human emotion is by observing a person's facial expressions and modulation of speech, and it can be categorized as sad, angry, happy, fearful, and so on. Emotion recognition using Brain Computer Interface (BCI) systems is beneficial for patients suffering from paralysis, autism, and mental retardation who cannot express their emotions like regular people. In this paper, after analyzing several data mining algorithms and various Neural Network models such as Convolution Neural Networks (CNN), Recurrent Neural Networks (RNN), and the Bi-directional RNN it has been proposed that Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) based emotion recognition using Electroencephalography (EEG) signals provides a better result. The main purpose of this paper is to introduce models which can work better than the existing ones on the K-EmoCon dataset. The metrics used in this paper are valence and arousal. The proposed RNN-LSTM model achieves a valence accuracy of 69.85% and an arousal accuracy of 45.07%. This model improves the accuracy of emotion detection on the K-EmoCon dataset. This approach achieves 4% more accuracy when compared to existing models such as the Convolution-augmented Transformer.

Keywords

Emotion Recognition, Neural Network Models, Bi-Directional RNN-LSTM, Valence, Arousal, K-EmoCon.

How to Cite this Article?

Devi, C. A., Renuka, D. K., Abishek, L. R., Kaaviya, A., Prediksha, R. L., and Yaswanth, M. (2023). RNN LSTM-Based Emotion Recognition using EEG Signals. i-manager’s Journal on Artificial Intelligence & Machine Learning, 1(1), 1-11. https://doi.org/10.26634/jaim.1.1.19112

References

[1]. Aguinaga, A. R., Ramírez, M. L., & Flores, M. D. R. B. (2015). Classification model of arousal and valence mental states by EEG signals analysis and Brodmann correlations. International Journal of Advanced Computer Science and Applications, 6(6), 230-238.

[2]. Aydin, S. G., Kaya, T., & Guler, H. (2016). Waveletbased study of valence–arousal model of emotions on EEG signals with LabVIEW. Brain Informatics, 3(2), 109-117.

[3]. Aydin, S. G., Kaya, T., & Guler, H. (2016). Wavelet-based study of valence–arousal model of emotions on EEG signals with LabVIEW. Brain Informatics, 3, 109-117.

[4]. Candra, H., Yuwono, M., Chai, R., Handojoseno, A., Elamvazuthi, I., Nguyen, H. T., & Su, S. (2015, August). Investigation of window size in classification of EEGemotion signal with wavelet entropy and support vector machine. In 2015 37th Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 7250-7253). IEEE.

[5]. Chen, J. X., Zhang, P. W., Mao, Z. J., Huang, Y. F., Jiang, D. M., & Zhang, Y. N. (2019). Accurate EEG-based emotion recognition on combined features using deep convolutional neural networks. IEEE Access, 7, 44317-44328.

[6]. Chen, Y. F., Cui, Y. L., & Wang, S. X. (2017). Review of emotion recognition based on physiological signals. System Simulation Technology, 13(1), 1-5.

[7]. Chung, S. Y., & Yoon, H. J. (2012, October). Affective classification using Bayesian classifier and supervised learning. In 2012 12th International Conference on Control, Automation and Systems (pp. 1768-1771). IEEE.

[8]. Koelstra, S., Muhl, C., Soleymani, M., Lee, J. S., Yazdani, A., Ebrahimi, T., ... & Patras, I. (2011). Deap: A database for emotion analysis; using physiological signals. IEEE Transactions on Affective Computing, 3(1), 18-31.

[9]. Koelstra, S., Muhl, C., Soleymani, M., Lee, J. S., Yazdani, A., Ebrahimi, T., ... & Patras, I. (2011). Deap: A database for emotion analysis; using physiological signals. IEEE Transactions on Affective Computing, 3(1), 18-31.

[10]. Martinez, H. P., Bengio, Y., & Yannakakis, G. N. (2013). Learning deep physiological models of affect. IEEE Computational Intelligence Magazine, 8(2), 20-33.

[11]. Park, C. Y., Cha, N., Kang, S., Kim, A., Khandoker, A. H., Hadjileontiadis, L., ... & Lee, U. (2020). K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations. Scientific Data, 7(1), 293.

[12]. Placidi, G., Di Giamberardino, P., Petracca, A., Spezialetti, M., & Iacoviello, D. (2016, November). Classification of Emotional Signals from the DEAP dataset. In Neurotechnix (pp. 15-21).

[13]. Reuderink, B., Mühl, C., & Poel, M. (2013). Valence, arousal and dominance in the EEG during game play. International Journal of Autonomous and Adaptive Communications Systems, 6(1), 45-62.

[14]. Tripathi, S., Acharya, S., Sharma, R., Mittal, S., & Bhattacharya, S. (2017, February). Using deep and convolutional neural networks for accurate emotion classification on DEAP data. In Proceedings of the AAAI Conference on Artificial Intelligence, 31 (2), 4746-4752.

[15]. Wichakam, I., & Vateekul, P. (2014, May). An evaluation of feature extraction in EEG-based emotion prediction with support vector machines. In 2014 11th International Joint Conference on Computer Science and Software Engineering (JCSSE) (pp. 106-110). IEEE.

[16]. Yang, K., Tag, B., Gu, Y., Wang, C., Dingler, T., Wadley, G., & Goncalves, J. (2022, June). Mobile emotion recognition via multiple physiological signals using convolution-augmented transformer. In Proceedings of the 2022 International Conference on Multimedia Retrieval (pp. 562-570).

[17]. Zheng, W. L., Zhu, J. Y., & Lu, B. L. (2017). Identifying stable patterns over time for emotion recognition from EEG. IEEE Transactions on Affective Computing, 10(3), 417-429.