International Journal of Computational and Electronic Aspects in Engineering
Volume 7 · Issue 2 · June 2026 · pp. 278-290
Review Article · Peer Reviewed
Received: April 05, 2026 · Accepted: June 08, 2026 · Published: June 28, 2026
DOI: 10.26706/ijceae.7.2.20260316
Open Access · CC BY 4.0

A Review on EEG-Based Emotion Recognition: Methods, Challenges, and Future Directions

Hiba Abdulkareem Saleh1,*, Omar Ibrahim Alsaif2
1Department of Chemical and Petroleum Industries Techniques Engineering, Technical Engineering College, Northern Technical University, Mosul, Iraq.
2Polytechnique College, Northern Technical University, Mosul, Iraq.

*Corresponding author: hiba.abdlkareem@ntu.edu.iq

Download PDF Cite this article Certificate Volume 7 Issue 2

Abstract

Electroencephalography (EEG) is a non-invasive and objective method to evaluate the emotional states of human beings with high temporal resolution and robustness to subtle or hidden facial expressions. This review systematically explores EEG-based emotion recognition from signal acquisition to preprocessing, feature extraction and classification. Both classical machine learning and modern deep learning frameworks are reviewed together with a comprehensive taxonomy of existing methods. Publicly available EEG datasets and benchmark results are compared to facilitate critical evaluation of different approaches. The review discusses major challenges including inter-subject variability, signal noise, and limited dataset sizes. Future research directions include optimization of deep learning architectures, multimodal fusion with physiological signals, transfer learning, and lightweight models for wearable EEG systems. Current evidence indicates that deep learning methods outperform conventional machine learning techniques, demonstrating significant potential for affective computing and human-computer interaction. Overall, EEG-based emotion recognition provides a reliable framework for advancing both academic research and practical intelligent systems.

Keywords

EEG Deep Learning Machine Learning Signal Processing

References

  1. K. Taghizad-Tavana, M. Ghanbari-Ghalehjoughi, A. Safari, M. T. Hagh, and A. E. Nezhad, “From green hydrogen production to artificial intelligence–driven energy management in hydrogen fuel cell electric vehicles: A comprehensive review of technologies, optimization techniques, international standards, and investment programs,” Applied Energy, vol. 399, Art. no. 126534, Dec. 2025, doi: 10.1016/j.apenergy.2025.126534.
  2. S. A. Goswami, S. Dave, and K. C. Patel, “The Need for Emotional Intelligence in Human-Computer Interactions,” in Harnessing Artificial Emotional Intelligence for Improved Human-Computer Interactions. Hershey, PA, USA: IGI Global Scientific Publishing, 2024, pp. 82–106, doi: 10.4018/979-8-3693-2794-4.ch006.
  3. N. S. Vanitha, B. N. Devi, A. Karthikeyan, K. Radhika, D. Anbuselvi, and S. G. Infantiya, “A Review of Artificial Emotional Intelligence for Human-Computer Interactions: Applications and Challenges,” in Harnessing Artificial Emotional Intelligence for Improved Human-Computer Interactions. Hershey, PA, USA: IGI Global Scientific Publishing, 2024, pp. 33–47, doi: 10.4018/979-8-3693-2794-4.ch003.
  4. P. K. Maroju and P. Bhattacharya, “Understanding Emotional Intelligence: The Heart of Human-Centered Technology,” in Advances in Computational Intelligence and Robotics, S. Tikadar, H. Liu, P. Bhattacharya, and S. Bhattacharya, Eds. IGI Global, 2024, pp. 1–18, doi: 10.4018/979-8-3693-7011-7.ch001.
  5. V. Pawar, A. Vhatkar, P. Chavan, S. Gawankar, and S. Nair, “The Future of Emotional Engineering: Integrating Generative AI and Emotional Intelligence,” in Proc. 8th Int. Conf. Computing, Communication, Control and Automation (ICCUBEA), Aug. 2024, pp. 1–6, doi: 10.1109/ICCUBEA61740.2024.10775105.
  6. M. Chawla, S. N. Panda, V. Khullar, I. Kansal, and R. Kumar, “Recent Trends in Machine and Deep Learning for Verbal and Non-verbal Emotion Detection,” Recent Advances in Electrical & Electronic Engineering, vol. 18, no. 7, pp. 903–922, Aug. 2025, doi: 10.2174/0123520965303142240430101645.
  7. S. S. Tippannavar, Y. S. D., P. K. M., M. S. M. P., C. M. B. N., and E. A. Madappa, “Advances and Challenges in Human Emotion Recognition Systems: A Comprehensive Review,” Journal of Trends in Computer Science and Smart Technology, vol. 5, no. 4, pp. 367–387, Dec. 2023, doi: 10.36548/jtcsst.2023.4.001.
  8. A. Dessai and H. Virani, “Emotion Classification Using Physiological Signals: A Recent Survey,” in Proc. IEEE Int. Conf. Signal Processing, Informatics, Communication and Energy Systems (SPICES), Mar. 2022, pp. 333–338, doi: 10.1109/SPICES52834.2022.9774240.
  9. W. Lin and C. Li, “Review of Studies on Emotion Recognition and Judgment Based on Physiological Signals,” Applied Sciences, vol. 13, no. 4, Art. no. 2573, 2023, doi: 10.3390/app13042573.
  10. Y. Cai, X. Li, and J. Li, “Emotion Recognition Using Different Sensors, Emotion Models, Methods and Datasets: A Comprehensive Review,” Sensors, vol. 23, no. 5, Art. no. 2455, Feb. 2023, doi: 10.3390/s23052455.
  11. J. A. Russell, “A Circumplex Model of Affect,” Journal of Personality and Social Psychology, vol. 39, no. 6, pp. 1161–1178, 1980, doi: 10.1037/h0077714.
  12. S. Schachter and J. Singer, “Cognitive, Social, and Physiological Determinants of Emotional State,” Psychological Review, vol. 69, no. 5, pp. 379–399, Sept. 1962, doi: 10.1037/h0046234.
  13. J. A. Urigüen and B. Garcia-Zapirain, “EEG Artifact Removal: State-of-the-Art and Guidelines,” Journal of Neural Engineering, vol. 12, no. 3, Art. no. 031001, June 2015, doi: 10.1088/1741-2560/12/3/031001.
  14. S. Koelstra et al., “DEAP: A Database for Emotion Analysis Using Physiological Signals,” IEEE Transactions on Affective Computing, vol. 3, no. 1, pp. 18–31, Jan. 2012, doi: 10.1109/T-AFFC.2011.15.
  15. Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, no. 7553, pp. 436–444, May 2015, doi: 10.1038/nature14539.
  16. Y. S. Can, B. Arnrich, and C. Ersoy, “Stress Detection in Daily Life Scenarios Using Smart Phones and Wearable Sensors: A Survey,” Journal of Biomedical Informatics, vol. 92, Art. no. 103139, Apr. 2019, doi: 10.1016/j.jbi.2019.103139.
  17. W. Lin and C. Li, “Review of Studies on Emotion Recognition and Judgment Based on Physiological Signals,” Applied Sciences, vol. 13, no. 4, Art. no. 2573, Feb. 2023, doi: 10.3390/app13042573.
  18. H. Shrara, H. Ammar, M. Nasseredine, J. Charara, and F. Sbeity, “An EEG-Based Emotion Recognition Study Using Machine Learning and Deep Learning,” in Proc. 7th Int. Conf. Advances in Biomedical Engineering (ICABME), Oct. 2023, pp. 125–129, doi: 10.1109/ICABME59496.2023.10293013.
  19. L. Tang and L. He, “EEG-Based Emotion Analysis Using Person-Event Network,” in Proc. IEEE Int. Conf. Systems, Man, and Cybernetics (SMC), Oct. 2023, pp. 4908–4913, doi: 10.1109/SMC53992.2023.10394326.
  20. P. Huy-Tung, I. Eun-Tack, O. Myeong-Seok, and G. Gwang-Yong, “EEG-Based Emotion Recognition: A Review and Emerging Paths,” IEEE Access, vol. 13, pp. 165037–165060, 2025, doi: 10.1109/ACCESS.2025.3610918.
  21. H. Men, S. Lo, and X. Guo, “Research Review and Prospects of EEG Technology in the Field of Space Emotion Cognition,” in Symbiotic Intelligence, H. Chai, D. W. N. Bao, Z. Guo, and P. F. Yuan, Eds. Singapore: Springer Nature, 2025, pp. 457–467, doi: 10.1007/978-981-96-3433-0_40.
  22. P. Bucci and S. Galderisi, “Physiologic Basis of the EEG Signal,” in Standard Electroencephalography in Clinical Psychiatry. Hoboken, NJ, USA: John Wiley & Sons, Ltd., 2011, pp. 7–12, doi: 10.1002/9780470974612.ch2.
  23. S. Bandyopadhyay, M. Z. Koubeissi, and N. J. Azar, “Physiologic Basis of EEG and Epilepsy,” in Epilepsy Board Review: A Comprehensive Guide, M. Z. Koubeissi and N. J. Azar, Eds. New York, NY, USA: Springer, 2017, pp. 3–13, doi: 10.1007/978-1-4939-6774-2_1.
  24. K. S. Park, “Electrical Signal from the Brain,” in Humans and Electricity: Understanding Body Electricity and Applications, K. S. Park, Ed. Cham, Switzerland: Springer International Publishing, 2023, pp. 173–197, doi: 10.1007/978-3-031-20784-6_8.
  25. F. L. da Silva, “EEG: Origin and Measurement,” in EEG-fMRI: Physiological Basis, Technique, and Applications, C. Mulert and L. Lemieux, Eds. Cham, Switzerland: Springer International Publishing, 2022, pp. 23–48, doi: 10.1007/978-3-031-07121-8_2.
  26. Y. Ma, Z. Li, L. Chen, Q. Liu, and L. Yang, “Workload Recognition of Real Navigation: A Deep Learning Approach Integrating EEG Scalogram and Eye-Tracking Data,” Ocean Engineering, vol. 348, Art. no. 124106, Mar. 2026, doi: 10.1016/j.oceaneng.2025.124106.
  27. Q. Zeng et al., “Materials and System Design for Self-Decision Bioelectronic Systems,” Advanced Materials, Early View, Art. no. e21164, 2025, doi: 10.1002/adma.202521164.
  28. Z. Lou, R. Wang, B. E. Y. Belmekki, and M.-S. Alouini, “Towards Bioelectric Signal-Enabled Human Healthcare Monitoring: State-of-the-Art, Design Strategies, Challenges, and Future,” npj Biomedical Innovations, vol. 3, no. 1, p. 7, Jan. 2026, doi: 10.1038/s44385-025-00061-7.
  29. I. Hussain and S. J. Park, “HealthSOS: Real-Time Health Monitoring System for Stroke Prognostics,” IEEE Access, vol. 8, pp. 213574–213586, Nov. 2020, doi: 10.1109/ACCESS.2020.3040437.
  30. Y. Qin, Y. Zhang, Y. Zhang, S. Liu, and X. Guo, “Application and Development of EEG Acquisition and Feedback Technology: A Review,” Biosensors, vol. 13, no. 10, Art. no. 930, Oct. 2023, doi: 10.3390/bios13100930.
  31. G. Andreea, P. Mădălina, and E. Ceuca, “Measurement and Analysis of Electroencephalogram (EEG) Signals Using Pre-Amplification and Filtering Techniques,” in Proc. IEEE 30th Int. Symp. Design and Technology in Electronic Packaging (SIITME), Oct. 2024, pp. 184–188, doi: 10.1109/SIITME63973.2024.10814851.
  32. D. R. Alkhorshid, S. F. Molaeezadeh, and M. R. Alkhorshid, “Electroencephalography Acquisition System: Analog Design,” Biomedical Instrumentation & Technology, vol. 54, no. 5, pp. 346–351, Sept. 2020, doi: 10.2345/0899-8205-54.5.346.
  33. J. Epinat-Duclos et al., “Evaluating Portable EEG: A Comparison Between Two Wireless Systems (EPOC Flex and LiveAmp) and the Wired BrainAmp System,” PeerJ, vol. 14, Art. no. e20416, Jan. 2026, doi: 10.7717/peerj.20416.
  34. N. S. Amer and S. B. Belhaouari, “EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive Review,” IEEE Access, vol. 11, pp. 143116–143142, 2023, doi: 10.1109/ACCESS.2023.3341419.
  35. R. Dilmaghani et al., “Design and Implementation of a Wireless Multi-Channel EEG Recording,” in Proc. 7th Int. Symp. Communication Systems, Networks & Digital Signal Processing (CSNDSP), Jul. 2010, pp. 741–746, doi: 10.1109/CSNDSP16145.2010.5580328.
  36. V. Roy and S. Shukla, “Designing Efficient Blind Source Separation Methods for EEG Motion Artifact Removal Based on Statistical Evaluation,” Wireless Personal Communications, vol. 108, 2019, doi: 10.1007/s11277-019-06470-3.
  37. V. Roy and S. Shukla, “Designing Efficient Blind Source Separation Methods for EEG Motion Artifact Removal Based on Statistical Evaluation,” Wireless Personal Communications, vol. 108, 2019, doi: 10.1007/s11277-019-06470-3.
  38. N. F. Afif, S. H. Pratama, F. Haryanto, S. N. Khotimah, and Suprijadi, “Comparison of Wet and Dry EEG Electrodes Based on Brain Signals Characterization in Temporal and Anterior Frontal Areas Using Audio Stimulation,” Journal of Physics: Conference Series, vol. 1505, no. 1, Art. no. 012069, 2020, doi: 10.1088/1742-6596/1505/1/012069.
  39. E. Habibzadeh Tonekabony Shad, M. Molinas, and T. Ytterdal, “Impedance and Noise of Passive and Active Dry EEG Electrodes: A Review,” IEEE Sensors Journal, vol. 20, no. 24, pp. 14565–14577, Dec. 2020, doi: 10.1109/JSEN.2020.3012394.
  40. T. O. Zander et al., “A Dry EEG-System for Scientific Research and Brain–Computer Interfaces,” Frontiers in Neuroscience, vol. 5, May 2011, doi: 10.3389/fnins.2011.00053.
  41. M. Zhang et al., “Recent advances in portable dry electrode EEG: Architecture and applications in brain-computer interfaces,” Sensors, vol. 25, no. 16, Art. no. 5215, 2025, doi: 10.3390/s25165215.
  42. H. Elmousalami, F. K. P. Hui, and L. Aye, “Electroencephalography (EEG) for psychological hazards and mental health in construction safety automation: Algorithmic systematic review (ASR),” Automation in Construction, vol. 177, Art. no. 106346, 2025, doi: 10.1016/j.autcon.2025.106346.
  43. A. Yeung et al., “Comparison of foam-based and spring-loaded dry EEG electrodes with wet electrodes in resting and moving conditions,” in Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015, pp. 7131–7134, doi: 10.1109/EMBC.2015.7320036.
  44. M. Sheeraz, W. Saadeh, and M. A. B. Altaf, “A wearable EEG acquisition device with flexible silver ink screen printed dry sensors,” in Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS), 2023, pp. 1–5, doi: 10.1109/ISCAS46773.2023.10181812.
  45. K. Markov, M. Elgendi, and C. Menon, “EEG-based headset sleep wearable devices,” npj Biosensing, vol. 1, no. 1, Art. no. 12, 2024, doi: 10.1038/s44328-024-00013-y.
  46. H. Liu, Y. Zhang, Y. Li, and X. Kong, “Review on emotion recognition based on electroencephalography,” Frontiers in Computational Neuroscience, vol. 15, 2021, doi: 10.3389/fncom.2021.758212.
  47. N. AL-Qawasmeh, C. Y. Suen, and E. Omar, “Emotion detection using EEG analysis: Insights into neural signatures and practical applications,” in Proceedings of the 7th Asia Conference on Cognitive Engineering and Intelligent Interaction (CEII), 2024, pp. 36–40, doi: 10.1109/CEII65291.2024.00016.
  48. M. N. Rusalova and M. B. Kostyunina, “Spectral correlation studies of emotional states in humans,” Neuroscience and Behavioral Physiology, vol. 34, no. 8, pp. 803–808, 2004, doi: 10.1023/B:NEAB.0000038131.14538.76.
  49. V. S. Ramaiah, R. Revathi, S. H. Ahammad, and I. H. Saeed, “An advanced EEG signal based emotion detection using machine learning and deep learning technique,” AIP Conference Proceedings, vol. 2919, no. 1, Art. no. 100008, 2024, doi: 10.1063/5.0190587.
  50. M. B. Kostyunina and M. A. Kulikov, “Frequency characteristics of EEG spectra in the emotions,” Neuroscience and Behavioral Physiology, vol. 26, no. 4, pp. 340–343, 1996, doi: 10.1007/BF02359037.
  51. M. Zokaei, M. J. Jafari, R. Khosrowabadi, A. Nahvi, S. Khodakarim, and M. Pouyakian, “Tracing the physiological response and behavioral performance of drivers at different levels of mental workload using driving simulators,” Journal of Safety Research, vol. 72, pp. 213–223, 2020, doi: 10.1016/j.jsr.2019.12.022.
  52. K. Yang, L. Tong, J. Shu, N. Zhuang, B. Yan, and Y. Zeng, “High gamma band EEG closely related to emotion: Evidence from functional network,” Frontiers in Human Neuroscience, vol. 14, 2020, doi: 10.3389/fnhum.2020.00089.
  53. P. Valdés-Alemán, B. Téllez-Alanís, D. Platas-Neri, and B. Palacios-Hernández, “Frontal alpha and parietal theta asymmetries associated with color-induced emotions,” Brain Research, vol. 1846, Art. no. 149297, 2025, doi: 10.1016/j.brainres.2024.149297.
  54. L. Yuan, J. Wei, and Y. Liu, “Spiking neural networks for EEG signal analysis using wavelet transform,” Frontiers in Neuroscience, vol. 19, 2025, doi: 10.3389/fnins.2025.1652274.
  55. N. Masood and H. Farooq, “Comparing neural correlates of human emotions across multiple stimulus presentation paradigms,” Brain Sciences, vol. 11, no. 6, Art. no. 696, 2021, doi: 10.3390/brainsci11060696.
  56. J. Li, X. Wu, Y. Zhang, H. Yang, and X. Wu, “DRS-Net: A spatial–temporal affective computing model based on multichannel EEG data,” Biomedical Signal Processing and Control, vol. 76, Art. no. 103660, 2022, doi: 10.1016/j.bspc.2022.103660.
  57. Q. Luo, X. Cheng, T. Holroyd, D. Xu, F. W. Carver, and J. Blair, “Theta band activity in response to emotional expressions and its relationship with gamma band activity as revealed by MEG and advanced beamformer source imaging,” Frontiers in Human Neuroscience, vol. 7, 2014, doi: 10.3389/fnhum.2013.00940.
  58. P. Patrikelis et al., “Emotional asymmetries in refractory medial temporal and frontal lobe epilepsy: Their impact on predicting lateralization and localization of seizures,” Epilepsy & Behavior, vol. 94, pp. 269–276, 2019, doi: 10.1016/j.yebeh.2019.03.008.
  59. E. Gkintoni and C. Halkiopoulos, “Mapping EEG metrics to human affective and cognitive models: An interdisciplinary scoping review from a cognitive neuroscience perspective,” Biomimetics, vol. 10, no. 11, Art. no. 730, 2025, doi: 10.3390/biomimetics10110730.
  60. X. Li, Z. Yan, P. Gong, D. Lin, and F. Chen, “Research progress on emotion recognition based on electroencephalogram signals,” Biomedical Signal Processing and Control, vol. 113, Art. no. 109188, 2026, doi: 10.1016/j.bspc.2025.109188.
  61. D. Dadebayev, W. W. Goh, and E. X. Tan, “EEG-based emotion recognition: Review of commercial EEG devices and machine learning techniques,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 7, pp. 4385–4401, Jul. 2022, doi: 10.1016/j.jksuci.2021.03.009.
  62. B. Ram, A. K. Jha, S. P. Sah, and S. Umapathy, “Multimodal Emotion Recognition via Correlation of EEG, Thermal, and Digital Image Data,” in Proc. 11th International Conference on Communication and Signal Processing (ICCSP), Jun. 2025, pp. 508–513, doi: 10.1109/ICCSP64183.2025.11088814.
  63. A. Prakash and A. Poulose, “Electroencephalogram-based emotion recognition: A comparative analysis of supervised machine learning algorithms,” Data Science and Management, vol. 8, no. 3, pp. 342–360, Sep. 2025, doi: 10.1016/j.dsm.2024.12.004.
  64. M. Liu et al., “Multi-scale graph convolutional EEG emotion recognition method driven by dynamic channel state labels,” Biomedical Signal Processing and Control, vol. 117, p. 109515, May 2026, doi: 10.1016/j.bspc.2026.109515.
  65. C. Yu, X. Zhang, X. Zhang, and Y. Yang, “EEG emotion recognition using multi-dimensional features with attention-based inception capsule network,” Biomedical Signal Processing and Control, vol. 110, p. 108248, Dec. 2025, doi: 10.1016/j.bspc.2025.108248.
  66. A. Mishra and P. Ranjan, “Comprehensive analysis of the state of the art on emotion recognition using EEG,” Computers and Electrical Engineering, vol. 131, p. 110958, Mar. 2026, doi: 10.1016/j.compeleceng.2026.110958.
  67. U. G. Redwan, T. Zaman, and H. B. Mizan, “Spatio-temporal CNN-BiLSTM dynamic approach to emotion recognition based on EEG signal,” Computers in Biology and Medicine, vol. 192, p. 110277, Jun. 2025, doi: 10.1016/j.compbiomed.2025.110277.
  68. Y. Zhao, S. Liu, L. Cao, Y. Ji, W. Wu, and B. Wang, “Electrode quantity outperforms spatial topology in EEG-based emotion recognition: A dual-CNN study on electrode configuration,” Biomedical Signal Processing and Control, vol. 113, p. 109082, Mar. 2026, doi: 10.1016/j.bspc.2025.109082.
  69. J. Chen, Y. Cui, C. Wei, K. Polat, and F. Alenezi, “Advances in EEG-based emotion recognition: Challenges, methodologies, and future directions,” Applied Soft Computing, vol. 180, p. 113478, Aug. 2025, doi: 10.1016/j.asoc.2025.113478.
  70. B. Mouazen, A. Benali, N. T. Chebchoub, E. H. Abdelwahed, and G. De Marco, “Enhancing EEG-Based Emotion Detection with Hybrid Models: Insights from DEAP Dataset Applications,” Sensors, vol. 25, no. 6, p. 1827, Mar. 2025, doi: 10.3390/s25061827.
  71. D. W. Prabowo, H. A. Nugroho, N. A. Setiawan, and J. Debayle, “A systematic literature review of emotion recognition using EEG signals,” Cognitive Systems Research, vol. 82, p. 101152, Dec. 2023, doi: 10.1016/j.cogsys.2023.101152.
  72. Y. Geng, S. Shi, and X. Hao, “Deep learning-based EEG emotion recognition: A comprehensive review,” Neural Computing and Applications, vol. 37, no. 4, pp. 1919–1950, Feb. 2025, doi: 10.1007/s00521-024-10821-y.
  73. N. Kukhilava et al., “Evaluation in EEG Emotion Recognition: State-of-the-Art Review and Unified Framework,” arXiv, May 14, 2025, Art. no. arXiv:2505.18175, doi: 10.48550/arXiv.2505.18175.
  74. B. Mouazen, A. Benali, N. T. Chebchoub, E. H. Abdelwahed, and G. D. Marco, “Enhancing EEG-Based Emotion Detection with Hybrid Models: Insights from DEAP Dataset Applications,” Sensors, vol. 25, no. 6, Mar. 2025, doi: 10.3390/s25061827.
  75. Y. Liu, W. Xue, L. Yang, and M. Li, “Deep Learning-Based EEG Emotion Recognition: A Review,” Brain Sciences, vol. 16, no. 1, p. 41, Dec. 2025, doi: 10.3390/brainsci16010041.
  76. S. Katsigiannis and N. Ramzan, “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, vol. 22, no. 1, pp. 98–107, Jan. 2018, doi: 10.1109/JBHI.2017.2688239.
  77. J. Panchal, M. I. Singh, K. S. Sandha, and M. Singh, “Multi-Database EEG Integration for Subject-Independent Emotion Recognition in Brain–Computer Interface Systems,” Mathematics, vol. 14, no. 3, Jan. 2026, doi: 10.3390/math14030474.
  78. E. H. Houssein, A. Hammad, and A. A. Ali, “Human emotion recognition from EEG-based brain–computer interface using machine learning: A comprehensive review,” Neural Computing and Applications, vol. 34, no. 15, pp. 12527–12557, Aug. 2022, doi: 10.1007/s00521-022-07292-4.
  79. S. S. Al-Hadithy, A. S. Abdalkafor, and B. Al-Khateeb, “Emotion recognition in EEG signals: Deep and machine learning approaches, challenges, and future directions,” Computers in Biology and Medicine, vol. 196, p. 110713, Sep. 2025, doi: 10.1016/j.compbiomed.2025.110713.
  80. W. Ma, Y. Zheng, T. Li, Z. Li, Y. Li, and L. Wang, “A comprehensive review of deep learning in EEG-based emotion recognition: Classifications, trends, and practical implications,” PeerJ Computer Science, vol. 10, p. e2065, May 2024, doi: 10.7717/peerj-cs.2065.
  81. J. Cho and H. Hwang, “Spatio-Temporal Representation of an Electroencephalogram for Emotion Recognition Using a Three-Dimensional Convolutional Neural Network,” Sensors, vol. 20, p. 3491, Jun. 2020, doi: 10.3390/s20123491.
  82. Z. Huang, Y. Ma, R. Wang, W. Li, and Y. Dai, “A Model for EEG-Based Emotion Recognition: CNN-Bi-LSTM with Attention Mechanism,” Electronics, vol. 12, p. 3188, Jul. 2023, doi: 10.3390/electronics12143188.
  83. V. Romaniuk and A. Kashevnik, “Cross-Dataset Emotion Valence Prediction Approach from 4-Channel EEG: CNN Model and Multi-Modal Evaluation,” Big Data and Cognitive Computing, vol. 9, no. 11, p. 280, Nov. 2025, doi: 10.3390/bdcc9110280.
  84. H. A. Hamzah and K. K. Abdalla, “EEG-based emotion recognition systems: Comprehensive study,” Heliyon, vol. 10, no. 10, Art. no. e31485, May 2024, doi: 10.1016/j.heliyon.2024.e31485.
  85. H. Hamzah and K. Abdalla, “EEG-Based Emotion Recognition Datasets for Virtual Environments: A Survey,” Applied Computational Intelligence and Soft Computing, vol. 2024, Aug. 2024, doi: 10.1155/2024/6091523.
  86. S. Toraman and Ö. Dursun, “GameEmo-CapsNet: Emotion Recognition from Single-Channel EEG Signals Using the 1D Capsule Networks,” Traitement du Signal, vol. 38, pp. 1689–1698, Dec. 2021, doi: 10.18280/ts.380612.
  87. L. Tabbaa et al., “VREED: Virtual Reality Emotion Recognition Dataset Using Eye Tracking and Physiological Measures,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 5, pp. 1–20, Dec. 2021, doi: 10.1145/3495002.
  88. L. Shuaiqi et al., “HBUED: An EEG Dataset for Emotion Recognition,” Journal of Affective Disorders, vol. 385, Art. no. 119397, May 2025, doi: 10.1016/j.jad.2025.119397.
  89. X. Jiang, G.-B. Bian, and Z. Tian, “Removal of Artifacts from EEG Signals: A Review,” Sensors, vol. 19, no. 5, p. 987, Jan. 2019, doi: 10.3390/s19050987.
  90. M. K. Islam, A. Rastegarnia, and Z. Yang, “Methods for Artifact Detection and Removal from Scalp EEG: A Review,” Neurophysiologie Clinique, vol. 46, nos. 4–5, pp. 287–305, Nov. 2016, doi: 10.1016/j.neucli.2016.07.002.
  91. P. Samal and M. F. Hashmi, “Role of Machine Learning and Deep Learning Techniques in EEG-Based BCI Emotion Recognition System: A Review,” Artificial Intelligence Review, vol. 57, no. 3, Art. no. 50, Feb. 2024, doi: 10.1007/s10462-023-10690-2.
  92. R. Sharma and H. K. Meena, “Emerging Trends in EEG Signal Processing: A Systematic Review,” SN Computer Science, vol. 5, no. 4, Art. no. 415, Apr. 2024, doi: 10.1007/s42979-024-02773-w.
  93. S. Phadikar, N. Sinha, R. Ghosh, and E. Ghaderpour, “Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-Heuristically Optimized Non-Local Means Filter,” Sensors, vol. 22, no. 8, p. 2948, Apr. 2022, doi: 10.3390/s22082948.
  94. M. Grilo et al., “Artifact Removal for Emotion Recognition Using Mutual Information and Epanechnikov Kernel,” Biomedical Signal Processing and Control, vol. 83, Art. no. 104677, May 2023, doi: 10.1016/j.bspc.2023.104677.
  95. Y. Qin, Q. Xu, T. Kujala, X. Wang, and F. Cong, “Evaluating Spatial Normalization for SVM-Based EEG Decoding: A Within- and Between-Subjects Perspective,” Biomedical Signal Processing and Control, vol. 116, Art. no. 109535, May 2026, doi: 10.1016/j.bspc.2026.109535.
  96. S. Davarzani, S. Masihi, M. Panahi, A. Olalekan Yusuf, and M. Atashbar, “A Comparative Study on Machine Learning Methods for EEG-Based Human Emotion Recognition,” Electronics, vol. 14, no. 14, p. 2744, Jan. 2025, doi: 10.3390/electronics14142744.
  97. H. Hamzah and K. Abdalla, “EEG-Based Emotion Recognition Systems: Comprehensive Study,” Heliyon, vol. 10, Art. no. e31485, May 2024, doi: 10.1016/j.heliyon.2024.e31485.
  98. D. W. Prabowo, H. A. Nugroho, N. A. Setiawan, and J. Debayle, “A Systematic Literature Review of Emotion Recognition Using EEG Signals,” Cognitive Systems Research, vol. 82, Art. no. 101152, Dec. 2023, doi: 10.1016/j.cogsys.2023.101152.
  99. E. Gkintoni, A. Aroutzidis, H. Antonopoulou, and C. Halkiopoulos, “From Neural Networks to Emotional Networks: A Systematic Review of EEG-Based Emotion Recognition in Cognitive Neuroscience and Real-World Applications,” Brain Sciences, vol. 15, no. 3, p. 220, Mar. 2025, doi: 10.3390/brainsci15030220.
  100. J. Zhang, Z. Yin, P. Chen, and S. Nichele, “Emotion Recognition Using Multi-Modal Data and Machine Learning Techniques: A Tutorial and Review,” Information Fusion, vol. 59, pp. 103–126, Jul. 2020, doi: 10.1016/j.inffus.2020.01.011.