Advanced EEG Signal Based Min to Mean Algorithm Approach For Human Emotion Taxonomy And Mental State Analysis
Article Sidebar
Main Article Content
Abstract
With electroencephalography (EEG) brain waves alone, it is full-scale phenomena in the field of computer-brain interface DNN, CNN, and SVM have improved detection and prediction accuracy in a number of researches during the last several years. But when it comes to recognizing global reliance, both deep learning and SVM have obvious limits. Pre-processing, extraction capabilities, and network design are the most common techniques used in deep learning models today, yet they are still unable to produce reliable results in noisy and sparse datasets. Any dataset, no matter how little or large, may suffer from poor SVM performance due to overlapping target instructions and boundaries. There are many different sorts of emotions that may be classified using the particular approach employed in this research. In order to get a whole picture of a person's mental state, it is best to use a "Min of mean” proposed technique. After comparison to the referential mean, a feeling is divided into one of four emotional quadrants. The MIN Max range is used to further split the emotion into 12 subcategories based on the amount of arousal. The proposed set of rules performed better than existing methods. Research on multi-class emotion reputation has shown that, compared to more recent studies, the proposed technique may be rather strong. It is possible to analyze a person's mental health by using the emotional spectrum, which has an accuracy rate of above 90%.
Article Details
References
Kimmatkar, Nisha Vishnupant, and Vijaya B. Babu. "Human emotion classification from brain EEG signal using the multimodal approach of the classifier." Proceedings of the 2018 International Conference on Intelligent Information Technology. 2018.
Kimmatkar, Nisha Vishnupant, and B. Vijaya Babu. "Novel Approach for Emotion Detection and Stabilizing Mental State by Using Machine Learning Techniques." Computers 10.3 (2021): 37.
Liu, Wei, et al. "Multimodal emotion recognition using deep canonical correlation analysis." arXiv preprint arXiv:1908.05349 (2019).
Liu, Yishu, and Guifang Fu. "Emotion recognition by deeply learned multi-channel textual and EEG features." Future Generation Computer Systems 119 (2021): 1-6.
Rahman, Md Asadur, et al. "Employing PCA and t-statistical approach for feature extraction and classification of emotion from multichannel EEG signal." Egyptian Informatics Journal 21.1 (2020): 23-35.
Nawaz, Rab, et al. "Comparison of different feature extraction methods for EEG-based emotion recognition." Biocybernetics and Biomedical Engineering 40.3 (2020): 910-926.
Li, Zina, et al. "Enhancing BCI-based emotion recognition using an improved particle swarm optimization for feature selection." Sensors 20.11 (2020): 3028.
Gannouni, Sofien, et al. "Emotion detection using electroencephalography signals and a zero-time windowing-based epoch estimation and relevant electrode identification." Scientific Reports 11.1 (2021): 1-17.
Ackermann, Pascal, et al. "EEG-based automatic emotion recognition: Feature extraction, selection and classification methods." 2016 IEEE 18th international conference on e-health networking, applications, and services (Healthcom). IEEE, 2016.
Pane, Evi Septiana, et al. "Identifying rules for electroencephalograph (EEG) emotion recognition and classification." 2017 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME). IEEE, 2017.
Ahirwal, Mitul Kumar, and Mangesh Ramaji Kose. "Emotion recognition system based on EEG signal: A comparative study of different features and classifiers." 2018 Second International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2018.
Ren, Fuji, Yindong Dong, and Wei Wang. "Emotion recognition based on physiological signals using brain asymmetry index and echo state network." Neural Computing and Applications 31.9 (2019): 4491-4501.
Ranganathan, Hiranmayi, Shayok Chakraborty, and Sethuraman Panchanathan. "Multimodal emotion recognition using deep learning architectures." 2016 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2016.
Raj, R., & Sahoo, D. S. S. . (2022). Detection of Botnet Using Deep Learning Architecture Using Chrome 23 Pattern with IOT. Research Journal of Computer Systems and Engineering, 2(2), 38:44. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/31
Nisha Vishnupant Kimmatkar and Vijay Babu. 2022. “The Study of Emotional Brain to Detect Emotions Using Brain EEG Signals and Improving Accuracy of Emotion Detection System Using Feature Selection Techniques."2022 5th International Conference on Machine Vision and Applications ICMVA 2022. Association for Computing Machinery, New York, NY, USA, 22–33. https://doi.org/10.1145/3523111.3523115
S. L. Bangare, “Classification of optimal brain tissue using dynamic region growing and fuzzy min-max neural network in brain magnetic resonance images”, Neuroscience Informatics, Volume 2, Issue 3, September 2022, 100019, ISSN 2772-5286, https://doi.org/10.1016/j.neuri.2021.100019.
S. L. Bangare, G. Pradeepini, S. T. Patil, “Implementation for brain tumor detection and three dimensional visualization model development for reconstruction”, ARPN Journal of Engineering and Applied Sciences (ARPN JEAS), Vol.13, Issue.2, ISSN 1819-6608, pp.467-473. 20/1/2018 http://www.arpnjournals.org/jeas/research_papers/rp_2018/jeas_0118_6691.pdf
S. L. Bangare, G. Pradeepini, S. T. Patil, “Implementation for brain tumor detection and three dimensional visualization model development for reconstruction”, ARPN Journal of Engineering and Applied Sciences (ARPN JEAS), Vol.13, Issue.2, ISSN 1819-6608, pp.467-473. 20/1/2018 http://www.arpnjournals.org/jeas/research_papers/rp_2018/jeas_0118_6691.pdf
Shanthi, D. N. ., & J, S. . (2022). Social Network Based Privacy Data Optimization Using Ensemble Deep Learning Architectures. Research Journal of Computer Systems and Engineering, 3(1), 62–66. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/43
S. L. Bangare, G. Pradeepini and S. T. Patil, "Brain tumor classification using mixed method approach," 2017 International Conference on Information Communication and Embedded Systems (ICICES), 2017, pp. 1-4, doi: 10.1109/ICICES.2017.8070748.
Xu Wu, Dezhi Wei, Bharati P. Vasgi, Ahmed Kareem Oleiwi, Sunil L. Bangare, Evans Asenso, "Research on Network Security Situational Awareness Based on Crawler Algorithm", Security and Communication Networks, vol. 2022, Article ID 3639174, 9 pages, 2022. https://doi.org/10.1155/2022/3639174.
Alrawashdeh, R., Al-Fawa’reh, M., & Mardini, W. (2022). Enhanced Epileptic Seizure diagnosis using EEG Signals with Support vector machine and Bagging Classifiers. International Journal of Communication Networks and Information Security (IJCNIS), 13(3). https://doi.org/10.17762/ijcnis.v13i3.5114
N. Shelke, S. Chaudhury, S. Chakrabarti, S. L. Bangare et al. “An efficient way of text-based emotion analysis from social media using LRA-DNN”, Neuroscience Informatics, Volume 2, Issue 3, September 2022, 100048, ISSN 2772-5286, https://doi.org/10.1016/j.neuri.2022.100048.
Suneet Gupta, Sumit Kumar, Sunil L. Bangare, Shibili Nuhmani, Arnold C. Alguno, Issah Abubakari Samori, “Homogeneous Decision Community Extraction Based on End-User Mental Behavior on Social Media”, Computational Intelligence and Neuroscience, vol. 2022, Article ID 3490860, 9 pages, 2022. https://doi.org/10.1155/2022/3490860.
Gururaj Awate, S. L. Bangare, G. Pradeepini and S. T. Patil, “Detection of Alzheimers Disease from MRI using Convolutional Neural Network with Tensorflow”, arXiv, https://doi.org/10.48550/arXiv.1806.10170
A. Seekoli, A. D. Gupta, M. Thaseen, N. Gopinath, et. al,, "Empirical Analysis of Image Recognition and Classification through Machine Learning and Transfer Learning Approaches using Multiple Regression Analysis," 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 2022, pp. 283-287, doi: 10.1109/ICACITE53722.2022.9823721.
J. Alanya-Beltran, P. Narang, Taufikin, S. L. Bangare, C. Valderrama-Zapata and S. Jaiswal, "An Empirical Analysis of 3D Image Processing by using Machine Learning-Based Input Processing for Man-Machine Interaction," 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 2022, pp. 2478-2482, doi: 10.1109/ICACITE53722.2022.9823699.
S. D. Pande and M. S. R. Chetty. (2018) Analysis of Capsule Network (Capsnet) Architectures and Applications, Journal of Advanced Research in Dynamical and Control Systems, Vol. 10, No. 10, pp. 2765-2771.
Serikbayeva, S., Tussupov, J. A., Sambetbayeva, M. A., Yerimbetova, A., Borankulova, G., & Tungatarova, A. (2022). A MODEL OF A DISTRIBUTED INFORMATION SYSTEM BASED ON THE Z39.50 PROTOCOL. International Journal of Communication Networks and Information Security (IJCNIS), 13(3). https://doi.org/10.17762/ijcnis.v13i3.5122
S. D. Pande and M. S. R. Chetty. (2019) Position Invariant Spline Curve Based Image Retrieval Using Control Points”, International Journal of Intelligent Engineering and Systems, Vol. 12, No. 4, pp. 177-191.
S. D. Pande and M. S. R. Chetty. (2019) Precise Approach for Modified 2 Stage Algorithm to Find CPs of Cubic Bezier Curve. 5th International Conference on Computing Communication Control and Automation (ICCUBEA).