EEG-Based Emotion Recognition Using CNN-LSTM: Dynamic Segmentation and Feature Fusion

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Published: Jul 30, 2025
DOI: https://doi.org/10.33093/ijoras.2025.7.2.8
Keywords:
EEG-based Emotion Recognition, Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Feature Fusion, Dynamic Segmentation

Main Article Content

Nazia Tabraiz
Faculty of Computing, Riphah International University (Pakistan)
https://orcid.org/0009-0003-8062-754X
Sadia Abdul Jabar
Faculty of Computing, Riphah International University (Pakistan)
Jawaid Iqbal
Faculty of Computing, Riphah International University (Pakistan)

Abstract

This study examines current developments and persistent difficulties in identifying emotions from EEG data, particularly when it comes to real-time systems. The need for precise, quick-response models has increased as interest in emotion-aware applications—from adaptive human-computer interfaces to mental health tools—increases. Although deep learning methods such as CNNs and LSTMs have demonstrated remarkable accuracy (up to 98%), a number of practical issues still need to be addressed, especially in the areas of delay minimization and data preprocessing. In order to improve recognition speed and reliability, the research presents real-time prioritization techniques and dynamic segmentation procedures. It also examines the wider socioeconomic and ethical implications of EEG-based systems and highlights important avenues for further study, such as multimodal feature fusion and dataset diversification.


 


Manuscript received:1 Mar 2025 | Revised: 23 Apr 2025 | Accepted: 8 May 2025 | Published: 30 Jul 2025

Article Details

How to Cite
Tabraiz, N. ., Jabar, . S. A. . ., & Iqbal, J. (2025). EEG-Based Emotion Recognition Using CNN-LSTM: Dynamic Segmentation and Feature Fusion . International Journal on Robotics, Automation and Sciences, 7(2), 86–95. https://doi.org/10.33093/ijoras.2025.7.2.8
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Vol. 7 No. 2 (2025): International Journal on Robotics, Automation and Sciences
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Articles
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

References

Yu, Z. Li, Z. Zang and Y. Liu, “Real-Time EEG-Based Emotion Recognition,” Sensors, vol. 23, no. 18, p. 7853, 2023.

DOI: https://doi.org/10.3390/s23187853

S. Wang, J. Qu, Y. Zhang and Y. Zhang, “Multimodal Emotion Recognition From EEG Signals and Facial Expressions,” IEEE Access, vol. 11, pp. 33061–33068, 2023.

DOI:https://doi.org/10.1109/ACCESS.2023.3263670

J. Pan, W. Fang, Z. Zhang, B. Chen, Z. Zhang and S. Wang, “Multimodal Emotion Recognition based on Facial Expressions, Speech, and EEG,” IEEE Open Journal of Engineering in Medicine and Biology, vol. 5, pp. 396-403, 2024.

DOI: https://doi.org/10.1109/OJEMB.2023.3240280

D. Nandini, J. Yadav, A. Rani and V. Singh, “Design of subject independent 3D VAD emotion detection system using EEG signals and machine learning algorithms,” Biomedical Signal Processing and Control, vol. 85, pp. 104894, 2023.

DOI: https://doi.org/10.1016/j.bspc.2023.104894

A. Nandi, F. Xhafa, L. Subirats and S. Fort, “Real-Time Emotion Classification Using EEG Data Stream in E-Learning Contexts,” Sensors, vol. 21, no. 5, p. 1589, 2021.

DOI: https://doi.org/10.3390/s21051589

R. Alhalaseh and S. Alasasfeh, “Machine-Learning-Based Emotion Recognition System Using EEG Signals,” Computers, vol. 9, no. 4, p. 95, 2020.

DOI: https://doi.org/10.3390/computers9040095

S. Akter, R.A. Prodhan, T.S. Pias, D. Eisenberg and J. Fresneda Fernandez, “M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity,” Sensors, vol. 22, no. 21, p. 8467, 2022.

DOI: https://doi.org/10.3390/s22218467

L. Abdel-Hamid, “An Efficient Machine Learning-Based Emotional Valence Recognition Approach Towards Wearable EEG,” Sensors, vol. 23, no. 3, p. 1255, 2023.

DOI: https://doi.org/10.3390/s23031255

A.M. Mutawa and A. Hassouneh, “Multimodal Real-Time patient emotion recognition system using facial expressions and brain EEG signals based on Machine learning and Log-Sync methods,” Biomedical Signal Processing and Control, vol. 91, pp. 105942, 2024.

DOI: https://doi.org/10.1016/j.bspc.2023.105942

N.-D. Mai, H.-T. Nguyen and W.-Y. Chung, “Real-Time On-Chip Machine-Learning-Based Wearable Behind-The-Ear Electroencephalogram Device for Emotion Recognition,” IEEE Access, vol. 11, pp. 47258–47271, 2023.

DOI: https://doi.org/10.1109/ACCESS.2023.3276244

E. Yildirim, Y. Kaya and F. Kilic, “A Channel Selection Method for Emotion Recognition From EEG Based on Swarm-Intelligence Algorithms,” IEEE Access, vol. 9, pp. 109889–109902, 2021.

DOI: https://doi.org/10.1109/ACCESS.2021.3100638

C. Tan, M. Šarlija and N. Kasabov, “NeuroSense: Short-term emotion recognition and understanding based on spiking neural network modelling of spatio-temporal EEG patterns,” Neurocomputing, vol. 434, pp. 137–148, 2021.

DOI: https://doi.org/10.1016/j.neucom.2020.12.098

Md. R. Islam et al., “Emotion Recognition From EEG Signal Focusing on Deep Learning and Shallow Learning Techniques,” IEEE Access, vol. 9, pp. 94601–94624, 2021.

DOI: https://doi.org/10.1109/ACCESS.2021.3091487

P. Patel, R.R and R.N. Annavarapu, “EEG-based human emotion recognition using entropy as a feature extraction measure,” Brain Informatics, vol. 8, no. 1, p. 20, 2021.

DOI: https://doi.org/10.1186/s40708-021-00141-5

H. Chao and L. Dong, “Emotion Recognition Using Three-Dimensional Feature and Convolutional Neural Network from Multichannel EEG Signals,” IEEE Sensors Journal, vol. 21, no. 2, pp. 2024-2034, 2021.

DOI: https://doi.org/10.1109/JSEN.2020.3020828

M. Algarni, F. Saeed, T. Al-Hadhrami, F. Ghabban and M. Al-Sarem, “Deep Learning-Based Approach for Emotion Recognition Using Electroencephalography (EEG) Signals Using Bi-Directional Long Short-Term Memory (Bi-LSTM),” Sensors, vol. 22, no. 8, p. 2976, 2022.

DOI: https://doi.org/10.3390/s22082976

Arjun, A.S. Rajpoot and M.R. Panicker, “Subject independent emotion recognition using EEG signals employing attention driven neural networks,” Biomedical Signal Processing and Control, vol. 75, p. 103547, 2022.

DOI: https://doi.org/10.1016/j.bspc.2022.103547

V. Doma and M. Pirouz, “A comparative analysis of machine learning methods for emotion recognition using EEG and peripheral physiological signals,” Journal of Big Data, vol. 7, no. 1, p. 18, 2020,

DOI: https://doi.org/10.1186/s40537-020-00289-7

M.M. Rahman et al., “Recognition of human emotions using EEG signals: A review,” Computers in Biology and Medicine, vol. 136, p. 104696, 2021.

DOI: https://doi.org/10.1016/j.compbiomed.2021.104696

M. Li, H. Xu, X. Liu and S. Lu, “Emotion recognition from multichannel EEG signals using K-nearest neighbor classification,” Technology and Health Care, vol. 26, no. 1_suppl, pp. 509-519, 2018.

DOI: https://doi.org/10.3233/THC-174836

A.N.N.M. Yosi, K.A. Sidek, H.S. Yaacob, M. Othman and A.Z. Jusoh, “Emotion recognition using electroencephalogram signal,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 15, no. 2, pp. 786, 2019.

DOI: https://doi.org/10.11591/ijeecs.v15.i2.pp786-793

M.K. Ahirwal and M.R. 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), pp. 472–476, 2018.

DOI: https://doi.org/10.1109/ICCMC.2018.8488044

N. Liu, Y. Fang, L. Li, L. Hou, F. Yang and Y. Guo, “Multiple Feature Fusion for Automatic Emotion Recognition Using EEG Signals,” 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 896–900, 2018.

DOI: https://doi.org/10.1109/ICASSP.2018.8462518

C. Qing, R. Qiao, X. Xu and Y. Cheng, “Interpretable Emotion Recognition Using EEG Signals,” IEEE Access, vol. 7, pp. 94160–94170, 2019.

DOI: https://doi.org/10.1109/ACCESS.2019.2928691

H. Chao, L. Dong, Y. Liu and B. Lu, “Emotion Recognition from Multiband EEG Signals Using CapsNet,” Sensors, vol. 19, no. 9, p. 2212, 2019.

DOI: https://doi.org/10.3390/s19092212

Q. Gao, C. Wang, Z. Wang, X. Song, E. Dong and Y. Song, “EEG based emotion recognition using fusion feature extraction method,” Multimedia Tools and Applications, vol. 79, no. 37-38, pp. 27057-27074, 2020.

DOI: https://doi.org/10.1007/s11042-020-09354-y

N.S. Suhaimi, J. Mountstephens and J. Teo, “EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities,” Computational Intelligence and Neuroscience, vol. 2020, pp. 1-19, 2020.

DOI: https://doi.org/10.1155/2020/8875426

T.B. Alakus, M. Gonen and I. Turkoglu, “Database for an emotion recognition system based on EEG signals and various computer games – GAMEEMO,” Biomedical Signal Processing and Control, vol. 60, pp. 101951, 2020.

DOI: https://doi.org/10.1016/j.bspc.2020.101951

T. Tuncer, S. Dogan and A. Subasi, “A new fractal pattern feature generation function based emotion recognition method using EEG,” Chaos Solitons Fractals, vol. 144, p. 110671, 2021.

DOI: https://doi.org/10.1016/j.chaos.2021.110671

H. Yang, J. Han and K. Min, “A Multi-Column CNN Model for Emotion Recognition from EEG Signals,” Sensors, vol. 19, no. 21, p. 4736, 2019.

DOI: https://doi.org/10.3390/s19214736

A.U. Rahman, A. Tubaishat, F. Al-Obeidat, Z. Halim, M. Tahir and F. Qayum, “Extended ICA and M-CSP with BiLSTM towards improved classification of EEG signals,” Soft Computing, vol. 26, no. 20, pp. 10687-10698, 2022.

DOI: https://doi.org/10.1007/s00500-022-06847-w

O. Bazgir, Z. Mohammadi and S.A.H. Habibi, “Emotion Recognition with Machine Learning Using EEG Signals,” 2018 25th National and 3rd International Iranian Conference on Biomedical Engineering (ICBME), pp. 1–5, 2018.

DOI: https://doi.org/10.1109/ICBME.2018.8703559

Z. Mohammadi, J. Frounchi and M. Amiri, “Wavelet-based emotion recognition system using EEG signal,” Neural Computing and Applications, vol. 28, no. 8, pp. 1985-1990, 2017.

DOI: https://doi.org/10.1007/s00521-015-2149-8

W.-L. Zheng, B.-N. Dong and B.-L. Lu, “Multimodal emotion recognition using EEG and eye tracking data,” 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5040–5043, 2014.

DOI: https://doi.org/10.1109/EMBC.2014.6944757

N. O’rinov and D. To‘xtasinova, “Development Of A Real-Time Emotion Recognition System Using Facial Expressions And Eeg Based On Machine Learning Methods And Deep Neural Networks,” Interpretation and researches, vol. 1, no. 22, 2024.

URL: http://interpretationandresearches.uz/index.php/iar/article/view/1688 [Accessed, May 09, 2024]

H.H. Tasleem, M. Ahmed, M.W. Arshad and M. Hamza, “Development and Evaluation of Machine Learning Models for Early Detection of Asymptomatic COVID-19 Patients Using Heart Rate and Oxygen Levels,” Informatica, vol. 49, no. 15, pp. 41–54, 2025.

DOI: https://doi.org/10.31449/inf.v49i15.7601

M.H. Ashraf and H. Alghamdi, "HFF-Net: A Hybrid Convolutional Neural Network for Diabetic Retinopathy Screening and Grading," Biomedical Technology, vol. 8, pp. 50–64, 2024.

DOI: https://doi.org/10.1016/J.BMT.2024.09.004

M.H. Ashraf, F. Jabeen, H. Alghamdi, M.S. Zia and M.S. Almutari, "HVD-Net: A Hybrid Vehicle Detection Network for Vision-Based Vehicle Tracking and Speed Estimation," Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 8, 2023.

DOI: https://doi.org/10.1016/j.jksuci.2023.101657

H. Alghamdi and T. Turki, "PDD-Net: Plant Disease Diagnoses Using Multilevel and Multiscale Convolutional Neural Network Features," Agriculture, vol. 13, no. 5, p. 1072, 2023. DOI: https://doi.org/10.3390/AGRICULTURE13051072

T.J. Jie, M.S. Sayeed, "Review on Detecting Pneumonia in Deep Learning," International Journal on Robotics, Automation and Sciences, vol. 6, no. 1, pp. 70–77, 2024.

DOI: https://doi.org/10.33093/ijoras.2024.6.1.10

K.B. Gan, Y.E. Teoh, "An Edge Convolution Neural Network Model for Plant Health Classification Using Camera," International Journal on Robotics, Automation and Sciences, vol. 7, no. 1, pp. 1–6, 2025.

DOI: https://doi.org/10.33093/ijoras.2025.7.1.1