A Novel Stack Ensembled Approach for Emotion Recognition from EEG Signals: Performance and Robustness Analysis
Keywords:
Emotion recognition, EEG signals, stack ensemble, random forest, AdaBoost, XGBoost, performance analysis, robustness analysisAbstract
Due to the complexity of electroencephalography signals' patterns, it is difficult to accurately identify emotions using EEG signals. In this paper, we present a novel approach that aims to improve the accuracy and reliability of emotion recognition by implementing a stack comprising robust analysis and performance. The paper's introductory section covers the basics of emotion recognition with an emphasis placed on the challenges and advantages associated with this process. Its ability to accurately categorize and identify emotions from EEG signals has applications in fields such as healthcare, computing, and human-computer interaction. The proposed stack-based method combines the three well-known algorithms for emotion recognition, namely XGBoost, Random Forest, and AdaBoost. These are known for their ability to handle different types of decision-making processes and data. By combining these three algorithms, we can leverage their strengths and improve the performance of this process. The suggested stack method combines the three widely used algorithms for emotion recognition. These are AdaBoost, XGBoost, and Random Forest, which are known for their exceptional ability to handle diverse decision-making processes, data, and more. The researchers believe that by combining these three components in a stack, we can leverage the advantages they have to offer. Through a comprehensive performance evaluation and experiments, we were able to show that the stack-based method performed better than other methods, such as Random Forest, LightGBM, and AdaBoost. In terms of its accuracy, the stack achieved a 99.21% success rate in performing emotion recognition tasks. The proposed method's robust analysis demonstrates its ability to handle the varying noise and variations in the signals sent by EEG. Its stack's resilience to environmental influences, artifacts, and individual differences ensures reliable and consistent emotion recognition in diverse scenarios. The findings of the research conducted by the researchers are valuable in helping develop new techniques for accurately identifying emotions using electroencephalography signals. The suggested stack method exhibited promising results in both robustness and accuracy, which paves the way for the development of more robust and efficient emotion recognition systems.
Downloads
References
A. Al-Nafjan, M. Hosny, Y. Al-Ohali, and A. Al-Wabil, “Review and classification of emotion recognition based on EEG brain-computer interface system research: A systematic review,” Appl. Sci., vol. 7, no. 12, 2017, doi: 10.3390/app7121239.
T. Alotaiby, F. E. A. El-Samie, S. A. Alshebeili, and I. Ahmad, “A review of channel selection algorithms for EEG signal processing,” EURASIP J. Adv. Signal Process., vol. 2015, no. 1, 2015, doi: 10.1186/s13634-015-0251-9.
N. K. Bhandari and M. Jain, “Emotion recognition and classification using Eeg: A review,” Int. J. Sci. Technol. Res., vol. 9, no. 2, pp. 1827–1836, 2020.
A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: A review,” J. Neural Eng., vol. 16, no. 3, 2019, doi: 10.1088/1741-2552/ab0ab5.
D. Dadebayev, W. W. Goh, and E. X. Tan, “EEG-based emotion recognition: Review of commercial EEG devices and machine learning techniques,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 7, pp. 4385–4401, 2022, doi: 10.1016/j.jksuci.2021.03.009.
M. Hamada, B. B. Zaidan, and A. A. Zaidan, “A Systematic Review for Human EEG Brain Signals Based Emotion Classification, Feature Extraction, Brain Condition, Group Comparison,” J. Med. Syst., vol. 42, no. 9, 2018, doi: 10.1007/s10916-018-1020-8.
C. Yu and M. Wang, “Survey of emotion recognition methods using EEG information,” Cogn. Robot., vol. 2, no. June, pp. 132–146, 2022, doi: 10.1016/j.cogr.2022.06.001.
J. Z. Lim, J. Mountstephens, and J. Teo, “Emotion recognition using eye-tracking: Taxonomy, review and current challenges,” Sensors (Switzerland), vol. 20, no. 8, pp. 1–21, 2020, doi: 10.3390/s20082384.
M. K. Kim, M. Kim, E. Oh, and S. P. Kim, “A review on the computational methods for emotional state estimation from the human EEG,” Comput. Math. Methods Med., vol. 2013, 2013, doi: 10.1155/2013/573734.
D. Panda, D. Das Chakladar, and T. Dasgupta, Multimodal system for emotion recognition using eeg and customer review, vol. 1112. Springer Singapore, 2020.
Z. He, Y. Zhong, and J. Pan, “An adversarial discriminative temporal convolutional network for EEG-based cross-domain emotion recognition,” Comput. Biol. Med., vol. 141, p. 105048, 2022, doi: 10.1016/j.compbiomed.2021.105048.
M. Soleymani, S. Asghari-esfeden, M. Pantic, and Y. Fu, “Continuous Emotion Detection Using Eeg Signals And Facial Expressions" Imperial College London , UK , 2 Northeastern University , USA , 3 University of Twente , Netherlands,” vol. 231287, no. 231287, pp. 3–8, 2013.
Shahakar, M. ., Mahajan, S. ., & Patil, L. . (2023). Load Balancing in Distributed Cloud Computing: A Reinforcement Learning Algorithms in Heterogeneous Environment. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2), 65–74. https://doi.org/10.17762/ijritcc.v11i2.6130
A. Al-Nafjan, K. Alharthi, and H. Kurdi, “Lightweight building of an electroencephalogram-based emotion detection system,” Brain Sci., vol. 10, no. 11, pp. 1–17, 2020, doi: 10.3390/brainsci10110781.
S. Kumar G S, N. Sampathila, and T. Tanmay, “Wavelet based machine learning models for classification of human emotions using EEG signal,” Meas. Sensors, vol. 24, no. November, p. 100554, 2022, doi: 10.1016/j.measen.2022.100554.
S. Zhang, C. Tang, and C. Guan, “Visual-to-EEG cross-modal knowledge distillation for continuous emotion recognition,” Pattern Recognit., vol. 130, p. 108833, 2022, doi: 10.1016/j.patcog.2022.108833.
P. Pandey and K. R. Seeja, “Subject independent emotion recognition from EEG using VMD and deep learning,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 5, pp. 1730–1738, 2022, doi: 10.1016/j.jksuci.2019.11.003.
S. Thejaswini, K. M. Ravikumar, L. Jhenkar, A. Natraj, and K. K. Abhay, “Analysis of EEG based emotion detection of DEAP and SEED-IV databases using SVM,” Int. J. Recent Technol. Eng., vol. 8, no. 1, pp. 207–211, 2019, doi: 10.2139/ssrn.3509130.
V. Doma and M. Pirouz, “A comparative analysis of machine learning methods for emotion recognition using EEG and peripheral physiological signals,” J. Big Data, vol. 7, no. 1, 2020, doi: 10.1186/s40537-020-00289-7.
K. Kamble and J. Sengupta, “A comprehensive survey on emotion recognition based on electroencephalograph (EEG) signals,” Multimed. Tools Appl., 2023, doi: 10.1007/s11042-023-14489-9.
Z. M. Wang, J. W. Zhang, Y. He, and J. Zhang, “EEG emotion recognition using multichannel weighted multiscale permutation entropy,” Appl. Intell., vol. 52, no. 10, pp. 12064–12076, 2022, doi: 10.1007/s10489-021-03070-2.
V. Khetani, Y. Gandhi and R. R. Patil, "A Study on Different Sign Language Recognition Techniques," 2021 International Conference on Computing, Communication and Green Engineering (CCGE), Pune, India, 2021, pp. 1-4, doi: 10.1109/CCGE50943.2021.9776399.
R. Allauddin Mulla, M. Eknath Pawar, S. S. Banait, S. N. Ajani, M. Pravin Borawake, and S. Hundekari, “Design and Implementation of Deep Learning Method for Disease Identification in Plant Leaf”, IJRITCC, vol. 11, no. 2s, pp. 278–285, Mar. 2023.
Elena Petrova, Predictive Analytics for Customer Churn in Telecommunications , Machine Learning Applications Conference Proceedings, Vol 1 2021.
W. Anandpwar, S. Barhate, S. Limkar, M. Vyawahare, S. N. Ajani, and P. Borkar, “Significance of Artificial Intelligence in the Production of Effective Output in Power Electronics”, IJRITCC, vol. 11, no. 3s, pp. 30–36, Mar. 2023.
Shivadekar, S., Kataria, B., Hundekari, S., Wanjale, K., Balpande, V. P., & Suryawanshi, R. (2023). Deep Learning Based Image Classification of Lungs Radiography for Detecting COVID-19 using a Deep CNN and ResNet 50. International Journal of Intelligent Systems and Applications in Engineering, 11(1s), 241-250.
Mutha, R., Lavate, S., Limkar, S. et al. HDFRMAH: design of a high-density feature representation model for multidomain analysis of human health issues. Soft Comput 27, 8493–8503 (2023). https://doi.org/10.1007/s00500-023-08311-9
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
