2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 12 , Issue 1 , PP: 70-83, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Siva Satya Sreedhar P. 1 , M. S. Minu 2 * , P. Vidyasri 3 , Habeeb Omotunde 4 , A. Tamizharasi 5 , R. Logarasu 6 , Rama Prabha K. P. 7 , V. Subashree 8
Doi: https://doi.org/10.54216/JISIoT.120106
Recently, Emotion detection utilizing EEG signals develops popularity in domain of Human-Computer Interaction (HCI). EEG (electroencephalography) is a non-invasive approach, which processes electrical action from the brain through electrodes located in the scalp. An emotion recognition approach could not only be significant for healthy people among them disabled persons for detecting emotional changes and is utilized for different applications. It is significant to realize that emotion recognition in EEG indications is a difficult task owing to difficult and subjective nature of emotions. In recent times, Machine learning (ML) algorithms like Random Forests or Support Vector Machines (SVM) and Deep Learning (DL) systems namely Recurrent Neural Network (RNN) or Convolutional Neural Network (CNN) are trained on EEG feature extracted and connected emotional labels for classifying the user emotional state. This study presents an Automated EEG-based Emotion Detection using Bonobo Optimizer with Deep Learning (AEEGED-BODL) technique on HCI applications. The goal of the study is to analyze the EEG signals for the classification of several kinds of emotions in HCI applications. To achieve this, the AEEGED-BODL technique uses Higuchi fractal dimension (HFD) approach for extracting features in the EEG signals. Besides, the AEEGED-BODL technique makes use of the quasi-recurrent neural network (QRNN) approach for the detection and classification of distinct kinds of emotions. Furthermore, the BO system was demoralized for optimum hyperparameter selection of QRNN model, which helps in attaining an improved detection rate. The simulation validation of AEEGED-BODL algorithm was simulated on EEG signal database. The comprehensive result stated best outcome of the AEEGED-BODL algorithm over other recent approaches
Brain-Computer Interface , Emotion Recognition , Human-Computer Interaction , EEG signals , Deep learning
[1] Chen, X., Cao, M., Wei, H., Shang, Z. and Zhang, L., 2021. Patient emotion recognition in human computer interaction system based on machine learning method and interactive design theory. Journal of Medical Imaging and Health Informatics, 11(2), pp.307-312.
[2] Wu, M., Hu, S., Wei, B. and Lv, Z., 2022. A novel deep learning model based on the ICA and Riemannian manifold for EEG-based emotion recognition. Journal of Neuroscience Methods, 378, p.109642.
[3] Chowdary, M.K., Nguyen, T.N. and Hemanth, D.J., 2021. Deep learning-based facial emotion recognition for human–computer interaction applications. Neural Computing and Applications, pp.1-18.
[4] Islam, M.R., Moni, M.A., Islam, M.M., Rashed-Al-Mahfuz, M., Islam, M.S., Hasan, M.K., Hossain, M.S., Ahmad, M., Uddin, S., Azad, A. and Alyami, S.A., 2021. Emotion recognition from EEG signal focusing on deep learning and shallow learning techniques. IEEE Access, 9, pp.94601-94624.
[5] Zhao, Y., Yang, J., Lin, J., Yu, D. and Cao, X., 2020, July. A 3D convolutional neural network for emotion recognition based on EEG signals. In 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1-6). IEEE.
[6] Wang, Z., Jiao, R. and Jiang, H., 2020. Emotion recognition using WT-SVM in human-computer interaction. Journal of New Media, 2(3), p.121.
[7] Houssein, E.H., Hammad, A. and Ali, A.A., 2022. Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review. Neural Computing and Applications, 34(15), pp.12527-12557.
[8] Islam, M.R., Islam, M.M., Rahman, M.M., Mondal, C., Singha, S.K., Ahmad, M., Awal, A., Islam, M.S. and Moni, M.A., 2021. EEG channel correlation based model for emotion recognition. Computers in Biology and Medicine, 136, p.104757.
[9] Khare, S.K. and Bajaj, V., 2020. Time–frequency representation and convolutional neural network-based emotion recognition. IEEE transactions on neural networks and learning systems, 32(7), pp.2901-2909.
[10] Shao, H.M., Wang, J.G., Wang, Y., Yao, Y. and Liu, J., 2019, May. EEG-based emotion recognition with deep convolution neural network. In 2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS) (pp. 1225-1229). IEEE.
[11] Wu, D., Zhang, J. and Zhao, Q., 2020. Multimodal fused emotion recognition about expression-EEG interaction and collaboration using deep learning. IEEE Access, 8, pp.133180-133189.
[12] Algarni, M., Saeed, F., Al-Hadhrami, T., Ghabban, F. and Al-Sarem, M., 2022. Deep learning-based approach for emotion recognition using electroencephalography (EEG) signals using Bi-directional long short-term memory (Bi-LSTM). Sensors, 22(8), p.2976.
[13] Zhang, H., 2020. Expression-EEG based collaborative multimodal emotion recognition using deep autoencoder. IEEE Access, 8, pp.164130-164143.
[14] Hwang, S., Hong, K., Son, G. and Byun, H., 2020. Learning CNN features from DE features for EEG-based emotion recognition. Pattern Analysis and Applications, 23, pp.1323-1335.
[15] Choi, D.Y., Kim, D.H. and Song, B.C., 2020. Multimodal attention network for continuous-time emotion recognition using video and EEG signals. IEEE Access, 8, pp.203814-203826.
[16] Gao, Z., Wang, X., Yang, Y., Li, Y., Ma, K. and Chen, G., 2020. A channel-fused dense convolutional network for EEG-based emotion recognition. IEEE Transactions on Cognitive and Developmental Systems, 13(4), pp.945-954.
[17] Iyer, A., Das, S.S., Teotia, R., Maheshwari, S. and Sharma, R.R., 2023. CNN and LSTM based ensemble learning for human emotion recognition using EEG recordings. Multimedia Tools and Applications, 82(4), pp.4883-4896.
[18] Baradaran, F., Farzan, A., Danishvar, S. and Sheykhivand, S., 2023. Customized 2D CNN Model for the Automatic Emotion Recognition Based on EEG Signals. Electronics, 12(10), p.2232.
[19] Zheng, X., Liu, X., Zhang, Y., Cui, L. and Yu, X., 2021. A portable HCI systemâoriented EEG feature extraction and channel selection for emotion recognition. International Journal of Intelligent Systems, 36(1), pp.152-176.
[20] Cheng, Y., Hu, K., Wu, J., Zhu, H. and Shao, X., 2021. Autoencoder quasi-recurrent neural networks for remaining useful life prediction of engineering systems. IEEE/ASME Transactions on Mechatronics, 27(2), pp.1081-1092.
[21] Mostafa, M.A., El-Hay, E.A. and Elkholy, M.M., 2023. Optimal low voltage ride through of wind turbine doubly fed induction generator based on bonobo optimization algorithm. Scientific Reports, 13(1), p.7778.
[22] Alakus, T.B.; Gonen, M.; Turkoglu, I. Database for an emotion recognition system based on EEG signals and various computer games—GAMEEMO. Biomed. Signal Process. Control 2020, 60, 101951
[23] Dessai, A.; Virani, H. Emotion Classification Based on CWT of ECG and GSR Signals Using Various CNN Models. Electronics 2023, 12, 2795. https://doi.org/ 10.3390/electronics12132795
[24] Yang, Eunmok, et al. "Optimal Fuzzy Logic Enabled EEG Motor Imagery Classification for Brain Computer Interface." IEEE Access (2023).
[25] Basma K. Eldrandaly. "ActivBench: Leveraging Human Activity Inference from Smartphone Sensors for Human Computer Interactions." Journal of Cognitive Human-Computer Interaction, Vol. 5, No. 2, 2023 ,PP. 45-62 (Doi : https://doi.org/10.54216/JCHCI.050205)
