Emotion recognition using honey badger algorithm and deep neural network methods

Tabarek Alwan Tuib; Mohammad-Reza Feizi-Derakhshi; Yaqdhan Mahmood Hussein; Fahad Taha Al-Dhief

Author(s)

Tabarek Alwan Tuib Computerized Intelligence Systems Laboratory, Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz,Tabriz,Iran; Department of Electronics and Communication, College of Engineering, Al Muthanna University,Samawah,Iraq

Mohammad-Reza Feizi-Derakhshi Computerized Intelligence Systems Laboratory, Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz,Tabriz,Iran

Yaqdhan Mahmood Hussein College of Engineering, Al Ayen University,Nasiriyah,Iraq; Department of Electronics and Communication, College of Engineering, Al Muthanna University,Samawah,Iraq

Fahad Taha Al-Dhief Faculty of Information Science and Technology,Universiti Kebangsaan Malaysia (UKM), Bangi,Malaysia

Abstract

Nowadays, there is a high attention paid to human emotion recognition by using the electroencephalogram (EEG) signals based on machine learning technology. However, because of the high emotional variation, emotion recognition based on EEG is considered a high challenge in terms of pattern recognition. On the other hand, the Honey Badger (HB) algorithm is used to recognize several emotions such as happy, sad, angry, and others. Thus, this algorithm could attain several functions such as optimal selection of features, selection of a classifier, and setting of parameters based on various emotion datasets. Therefore, this paper presents a new method for human emotion recognition based on EEG signals. In the proposed method, the signals of the EEG are collected from the DREAMER database. Furthermore, the features of EEG signals are extracted using the Wavelet Transform (WT). The HB algorithm is used to select the most effective features of EEG signals and feed them to the classifier. For the classification part, there are several classifiers of Deep Neural Networks (DNNs) are used to classify the emotion classes. Experimental results show that the proposed method can achieve encouraging results with an accuracy of 82.5%. The proposed method using the HB algorithm demonstrates better performance than the standard features for analyzing EEG.

Publication Details

Page(s) 471-482

DOI DOI not available

Published Journal: ARPN Journal of Engineering and Applied Sciences, Volume: 20, Issue: 8, Year: 2025

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Keywords

Feature selection, Deep Neural Networks, Emotion Recognition, EEG, honey badger algorithm

References

Jafari M. .2023 .Emotion recognition in EEG signals using deep learning methods: A review. Computers in Biology and Medicine, : 107450.

Demir Z. .2020 .The role of emotion regulation as a mediator between early life stress and posttraumatic stress disorder, depression, and anxiety in Syrian refugees. Translational Psychiatry, 10(1) : 371.

Mallick R.,Flathmann C.,Lancaster C.,Hauptman A.,McNeese N.,Freeman G. .2023 .The pursuit of happiness: The power and influence of AI teammate emotion in human-AI teamwork. Behaviour & Information Technology, : 1-25.

Gao Q.,Wang Z.,Wang Y.,Song Y. .2020 .EEG-based emotion recognition using a fusion feature extraction method. Multimedia Tools and Applications, 79 : 27057-27074.

Albadr M. A. A.,Tiun S.,Al-Dhief F. T. .2018 .Evaluation of machine translation systems and related procedures. , : .

Albadra M. A. A.,Tiuna S. .2017 .Extreme learning machine: A review. International Journal of Applied Engineering Research, 12(14) : 4610-4623.

Albadr M. A. A.,Tiun S.,Ayob M.,Al-Dhief F. T.,Omar K.,Hamzah F. A. .2020 .Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection. PLOS ONE, 15(12) : e0242899.

Obaid O. I.,Mohammed M. A.,Ghani M.,Mostafa A.,Taha F. .2018 .Evaluating the performance of machine learning techniques in the classification of Wisconsin Breast Cancer. International Journal of Engineering & Technology. 7, (4) : 160-166.

Albadr M. A. A.,Tiun S.,AL-Dhief F. T.,Sammour M. A. .2018 .Spoken language identification based on the enhanced self-adjusting extreme learning machine approach. PLOS ONE, 13(4) : e0194770.

Razzaq Abed Alameer H.,Salehpour P.,Aghdasi and M. -R. H. S. .2025 ."Integrating Deep Metric Learning, Semi Supervised Learning, and Domain Adaptation for Cross-Dataset EEGBased Emotion Recognition,". in IEEE Access, 3536549 : 38914-38924.

Rajesh K.,Naveenkumar M. .2016 .A robust method for face recognition and face emotion detection system using support vector machines. Computer and Optimization Techniques (ICEECCOT), : 1-5.

Tuib T. A.,Hussein Y. M.,Anooz R. S. A.,Mohammed A. A.,Al-Dhief F. T. .2023 .An Array Antenna Based on Substrate Integrated Waveguide Antenna For 5G Application. International Conference on Communication and Information Technology (AICCIT), 10218059 : 60-63.

Semertzidis N.,Zambetta F.,Mueller F. F. .2023 .Brain-Computer Integration: A Framework for the Design of Brain-Computer Interfaces from an Integration Perspective. , : .

Baghbani F.,Akbarzadeh-T M.-R.,Akbarzadeh A. .2020 .Emotional neural networks with universal approximation property for stable direct adaptive nonlinear control systems. Engineering Applications of Artificial Intelligence, 89(103447) : .

Alwan Tuib T.,Saoudi B. H.,Hussein Y. M.,Mandeel T. H.,Al-Dhief F. T. .2024 .Convolutional neural network with binary moth flame optimization for emotion detection in electroencephalogram. IAES International Journal of Artificial Intelligence (IJ-AI)., : .

Salankar N.,Mishra P.,Garg L. .2021 .Emotion recognition from EEG signals using empirical mode decomposition and second-order difference plot. Biomedical Signal Processing and Control, 65 : 102389.

Demir F.,Sobahi N.,Siuly S.,Sengur A. .2021 .Exploring deep learning features for automatic classification of human emotion using EEG rhythms. IEEE Sensors Journal, 21(13) : 14923-14930.

Hadi Baydaa,Saoudi Baydaa . .Machine learning technique for detecting diabetes disease. ARPN Journal of Engineering and Applied Sciences., : .

Yudhana A.,Muslim A.,Wati D. E.,I. Puspitasari D. E.,Azhari A.,Mardhia M. M.,K-Nearest Neighbour M. M. .2020 .Human emotion recognition based on EEG signal using Fast Fourier Transform and. Adv. Sci. Technol, 5(6) : 1082-1088.

Son G.,Kim Y. .2021 .EEG-based emotion classification for verifying the Korean emotional movie clips with support vector machine (SVM). Complexity, : 1-14.

Laghrissi F.,Douzi S.,Douzi K.,Hssina B. .2021 .Intrusion detection systems using long short-term memory (LSTM). Journal of Big Data, 8(1) : 65s.

Sohn .2021 .Deep belief network-based intrusion detection techniques: A survey. Expert Systems with Applications, 167 : 114170.

Kattenborn T.,Leitloff J.,Schiefer F.,Hinz S. .2021 .Review on Convolutional Neural Networks (CNN) in vegetation remote sensing. ISPRS journal of photogrammetry and remote sensing, 173 : 24-49.

Chen P.,Zhang Z.,Mao Y.,Huang D.,Jiang Y.,Zhang Y. .2019 .Accurate EEG-based emotion recognition on combined features using deep convolutional neural networks. IEEE Access, 7 : 44317-44328.

Nguyen B. H.,Xue B.,Zhang M. .2020 .A survey on swarm intelligence approaches to feature selection in data mining. Swarm and Evolutionary Computation, 54 : 100663.

Darwish A.,Hassanien A. E.,Das S. .2020 .A survey of swarm and evolutionary computing approaches for deep learning. Artificial intelligence review, 53 : 1767-1812.

Pourpanah F.,Wang R.,Lim C. P.,Wang X.-Z.,Yazdani D. .2023 .A review of artificial fish swarm algorithms: Recent advances and applications. Artificial Intelligence Review, 56(3) : 1867-1903.

Umar S. U.,Rashid T. A. .2021 .Critical analysis: Bat algorithm-based investigation and application on several domains. World Journal of Engineering, 18(4) : 606-620.

Kumar V.,Kumar D. .2021 .A systematic review on firefly algorithm: past, present, and future. Archives of Computational Methods in Engineering, 28 : 3269-3291.

T. M. Shami A. A.,El-Saleh M.,Alswaitti Q.,AlTashi M. A.,Summakieh S.,Mirjalili S. .2022 .Particle swarm optimization: A comprehensive survey. IEEE Access, 10 : 10031-10061.

Singh S. S.,Singh K.,Kumar A.,Biswas B. .2020 .ACO-IM: maximizing influence in social networks using ant colony optimization. Soft Computing, 24(13) : 10181-10203.

Kaya E.,Gorkemli B.,Akay B.,Karaboga D. .2022 .A review of the studies employing the artificial bee colony algorithm to solve combinatorial optimization problems. Engineering Applications of Artificial Intelligence, 115 : 105311.

Hashim F. A.,Houssein E. H.,Hussain K.,Mabrouk M. S.,Al-Atabany W. .2022 .Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems. , 192 : 84-110.

He Z.,Zhong Y.,Pan J. .2022 .Joint temporal convolutional networks and adversarial discriminative domain adaptation for EEG-based cross-subject emotion recognition. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), : 3214-3218.

Li Z. .2020 .Enhancing BCI-based emotion recognition using an improved particle swarm optimization for feature selection. Sensors, 20(11) : 3028.

Gao Z.,Li Y.,Yang Y.,Wang X.,Dong and H.-D. Chiang N. .2020 .A GPSO-optimized convolutional neural network for EEG-based emotion recognition. Neurocomputing, 380 : 225-235.

He H.,Tan Y.,Ying J.,Zhang W. .2020 .Strengthen EEG-based emotion recognition using firefly integrated optimization algorithm. Applied Soft Computing, 94 : 106426.

Cui H.,Liu A.,Zhang X.,Chen X.,Wang K.,Chen X. .2020 .EEG-based emotion recognition using an end-to-end regional-asymmetric convolutional neural network. Knowledge-Based Systems, 205 : 106243.

Zhang Y. .2020 .An investigation of deep learning models for EEG-based emotion recognition. Frontiers in Neuroscience, 14 : 622759.

Hassouneh A.,Mutawa A.,Murugappan M. .2020 .Development of a real-time emotion recognition system using facial expressions and EEG based on machine learning and deep neural network methods. Informatics in Medicine Unlocked, 20 : 100372.

Kim Y.,Choi A. .2020 .EEG-based emotion classification using a long short-term memory network with an attention mechanism. Sensors, 20(23) : 6727.

Hwang S.,Hong K.,Byun H. .2020 .Learning CNN features from DE features for EEGbased emotion recognition. Pattern Analysis and Applications, 23 : 1323-1335.

Asghar M. A. .2019 .EEG-based multi-modal emotion recognition using bag of deep features: An optimal feature selection approach. Sensors, 19(23) : 5218.

Katsigiannis S.,Ramzan N. .2017 .DREAMER: A database for emotion recognition through EEG and ECG signals from wireless, low-cost off-the-shelf devices. IEEE journal of biomedical and health informatics, 22(1) : 98-107.

Albaqami H.,Hassan G. M.,Subasi A.,Datta A. .2021 .Automatic detection of abnormal EEG signals using wavelet feature extraction and gradient boosting decision tree. Biomedical Signal Processing and Control, 70 : 102957.