Optimizing Gated Recurrent Unit Architecture for Enhanced EEG-Based Emotion Classification
DOI:
https://doi.org/10.18196/jrc.v6i3.26016Keywords:
EEG-based Emotion Classification, BCI, Affective Computing, Gated Recurrent Unit, Temporal Feature ExtractionAbstract
Emotion recognition using EEG signals has gained significant attention in affective computing and brain-computer interface (BCI) applications. However, achieving high classification accuracy remains a challenge due to the complexity and variability of EEG signals. This study aims to optimize the Gated Recurrent Unit (GRU) model for improving the performance of EEG-based emotion classification. The approach involves feature selection and architectural modifications to the GRU model. Selected EEG features include mean, standard deviation, statistical moments (skewness and kurtosis), min-max values, logarithmic covariance matrix, covariance matrix, Shannon entropy, log-energy entropy, Fast Fourier Transform, and autocorrelation, extracted from alpha and beta frequency bands. The proposed GRU model consists of four stacked GRU layers with decreasing hidden state sizes, ensuring efficient temporal feature extraction while reducing computational complexity. Experimental results demonstrate the superiority of the proposed GRU model compared to Simple RNN, LSTM, and traditional Machine Learning models (Naïve Bayes, SVM, Random Forest, and Linear Regression). The GRU model achieves high recall (98.81%), specificity (99.42%), precision (98.82%), accuracy (99.22%), and F1 Score (98.81%), outperforming alternative models in all evaluation metrics. These findings indicate that the GRU model effectively captures temporal dependencies in EEG signals, making it a robust and efficient approach for EEG-based emotion classification. In conclusion, this research confirms that GRU is an optimal deep learning model for emotion recognition using EEG. Future research could explore multi-modal emotion recognition, attention-based architectures, and real-time deployment in wearable EEG devices to further enhance classification accuracy and real-world applicability.
References
A. Prakash and A. Poulose, “Electroencephalogram-Based Emotion Recognition: A Comparative Analysis of Supervised Machine Learning Algorithms,” Data Science and Management, Jan. 2025, doi: 10.1016/j.dsm.2024.12.004.
D. S. Menefee, T. Ledoux, and C. A. Johnston, “The Importance of Emotional Regulation in Mental Health,” American Journal of Lifestyle Medicine, vol. 16, no. 1, pp. 28–31, Jan. 2022, doi: 10.1177/15598276211049771.
M. Magalhães, A. Coelho, M. Melo, and M. Bessa, “Measuring users’ emotional responses in multisensory virtual reality: a systematic literature review,” Multimedia tools and applications, vol. 83, no. 14, pp. 43377–43417, Oct. 2023, doi: 10.1007/s11042-023-16918-1.
C. Yu and M. Wang, “Survey of emotion recognition methods using EEG information,” Cognitive Robotics, vol. 2, pp. 132–146, 2022, doi: 10.1016/j.cogr.2022.06.001.
K. Erat, E. B. Şahin, F. Doğan, N. Merdanoğlu, A. Akcakaya, and P. O. Durdu, “Emotion recognition with EEG-based brain-computer interfaces: a systematic literature review,” Multimedia tools and applications, vol. 83, no. 33, pp. 79647-79694, 2024, doi: 10.1007/s11042-024-18259-z.
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, 2022, doi: 10.1007/s00521-022-07292-4.
T. Warbrick, “Simultaneous EEG-fMRI: What Have We Learned and What Does the Future Hold?,” Sensors, vol. 22, no. 6, p. 2262, Mar. 2022, doi: 10.3390/s22062262.
G. Papanastasiou, A. Drigas, C. Skianis, and M. Lytras, “Brain computer interface-based applications for training and rehabilitation of students with neurodevelopmental disorders. A literature review,” Heliyon, vol. 6, no. 9, p. e04250, Sep. 2020, doi: 10.1016/j.heliyon.2020.e04250.
R. Rai, K. Rai, A S. Mamatha, and K. Sooda, “Advancements in epilepsy classification: Current trends and future directions,” MethodsX, vol. 14, p. 103257, Jun. 2025, doi: 10.1016/j.mex.2025.103257.
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, Feb. 2024, doi: 10.1007/s10462-023-10690-2.
H. Albaqami, G. M. Hassan, A. Subasi, and A. Datta, “Automatic detection of abnormal EEG signals using wavelet feature extraction and gradient boosting decision tree,” Biomedical Signal Processing and Control, vol. 70, p. 102957, Sep. 2021, doi: 10.1016/j.bspc.2021.102957.
J.-G. Wang, H.-M. Shao, Y. Yao, J.-L. Liu, H.-P. Sun, and S.-W. Ma, “Electroencephalograph-based emotion recognition using convolutional neural network without manual feature extraction,” Applied Soft Computing, vol. 128, p. 109534, Oct. 2022, doi: 10.1016/j.asoc.2022.109534.
V. Marques, Alexandria, J. Alexandre, D. Ferreira, P. C. Motta, and B. Riccelli, “Emotion Detection from EEG Signals Using Machine Deep Learning Models,” Bioengineering, vol. 11, no. 8, pp. 782–782, Aug. 2024, doi: 10.3390/bioengineering11080782.
J.-H. Lee, J.-Y. Kim, and H.-G. Kim, “Emotion Recognition Using EEG Signals and Audiovisual Features with Contrastive Learning,” Bioengineering, vol. 11, no. 10, p. 997, Oct. 2024, doi: 10.3390/bioengineering11100997.
D. Kulkarni and V. V. Dixit, “EEG-based emotion classification Model: Combined model with improved score level fusion,” Biomedical Signal Processing and Control, vol. 95, pp. 106352–106352, Apr. 2024, doi: 10.1016/j.bspc.2024.106352.
J. Dai, X. Xi, G. Li, and T. Wang, “EEG-Based Emotion Classification Using Improved Cross-Connected Convolutional Neural Network,” Brain Sciences, vol. 12, no. 8, p. 977, Jul. 2022, doi: 10.3390/brainsci12080977.
S. Reis, L. P.-Coelho, M. Sousa, M. Neto, and M. Silva, “Advancing Emotion Recognition: EEG Analysis and Machine Learning for Biomedical Human–Machine Interaction,” BioMedInformatics, vol. 5, no. 1, pp. 5–5, Jan. 2025, doi: 10.3390/biomedinformatics5010005.
M. Zuo, X. Chen, and L. Sui, “Evaluation of Machine Learning Algorithms for Classification of Visual Stimulation-Induced EEG Signals in 2D and 3D VR Videos,” Brain Sciences, vol. 15, no. 1, pp. 75–75, Jan. 2025, doi: 10.3390/brainsci15010075.
F. Li, F. Yu, L. Shen, H. Li, X. Yang, and Q. Shen, “EEG-Based Emotion Recognition with Combined Fuzzy Inference via Integrating Weighted Fuzzy Rule Inference and Interpolation,” Mathematics, vol. 13, no. 1, p. 166, Jan. 2025, doi: 10.3390/math13010166.
J. A. Cruz-Vazquez, J. Y. Montiel-Pérez, R. Romero-Herrera, and E. Rubio-Espino, “Emotion Recognition from EEG Signals Using Advanced Transformations and Deep Learning,” Mathematics, vol. 13, no. 2, p. 254, Jan. 2025, doi: 10.3390/math13020254.
S. Gannouni, A. Aledaily, K. Belwafi, and H. Aboalsamh, “Emotion detection using electroencephalography signals and a zero-time windowing-based epoch estimation and relevant electrode identification,” Scientific Reports, vol. 11, no. 1, Mar. 2021, doi: 10.1038/s41598-021-86345-5.
J. T. Bird, L. Manso, E. P. Ribeiro, A. Ekárt, and D. R. Faria, “A Study on Mental State Classification using EEG-based Brain-Machine Interface,” International Conference on Intelligent Systems, pp. 795-800, Sep. 2018, doi: 10.1109/is.2018.8710576.
B. Tripathi and R. K. Sharma, “EEG-Based Emotion Classification in Financial Trading Using Deep Learning: Effects of Risk Control Measures,” Sensors, vol. 23, no. 7, p. 3474, Mar. 2023, doi: 10.3390/s23073474.
J. J. Bird, D. R. Faria, L. J. Manso, A. Ekárt, and C. D. Buckingham, “A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction,” Complexity, vol. 2019, pp. 1–14, Mar. 2019, doi: 10.1155/2019/4316548.
Y. Pamungkas and U. W. Astuti, “Comparison of Human Emotion Classification on Single-Channel and Multi-Channel EEG using Gate Recurrent Unit Algorithm,” 2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE), pp. 1-6, Feb. 2023, doi: 10.1109/iccosite57641.2023.10127686.
A. Chaddad, Y. Wu, R. Kateb, and A. Bouridane, “Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques,” Sensors, vol. 23, no. 14, pp. 6434–6434, Jul. 2023, doi: 10.3390/s23146434.
A. D. Wibawa, N. Fatih, Y. Pamungkas, M. Pratiwi, P. A. Ramadhani, and Suwadi, “Time and Frequency Domain Feature Selection Using Mutual Information for EEG-based Emotion Recognition,” 2022 9th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), pp. 19-24, Oct. 2022, doi: 10.23919/eecsi56542.2022.9946522.
S. Morales and M. E. Bowers, “Time-frequency analysis methods and their application in developmental EEG data,” Developmental Cognitive Neuroscience, vol. 54, p. 101067, 2022, doi: 10.1016/j.dcn.2022.101067.
H. Azami et al., “EEG Entropy in REM Sleep as a Physiologic Biomarker in Early Clinical Stages of Alzheimer’s Disease,” Journal of Alzheimer s Disease, vol. 91, no. 4, pp. 1557–1572, Jan. 2023, doi: 10.3233/jad-221152.
J. Mathew, N. Sivakumaran, and P. A. Karthick, “Automated Detection of Seizure Types from the Higher-Order Moments of Maximal Overlap Wavelet Distribution,” Diagnostics, vol. 13, no. 4, p. 621, Feb. 2023, doi: 10.3390/diagnostics13040621.
A. Topic and M. Russo, “Emotion recognition based on EEG feature maps through deep learning network,” Engineering Science and Technology, an International Journal, vol. 24, no. 6, pp. 1442-1454, Apr. 2021, doi: 10.1016/j.jestch.2021.03.012.
I. Stancin, M. Cifrek, and A. Jovic, “A Review of EEG Signal Features and Their Application in Driver Drowsiness Detection Systems,” Sensors, vol. 21, no. 11, p. 3786, May 2021, doi: 10.3390/s21113786.
H. Liu, Y. Zhang, Y. Li, and X. Kong, “Review on Emotion Recognition Based on Electroencephalography,” Frontiers in Computational Neuroscience, vol. 15, Oct. 2021, doi: 10.3389/fncom.2021.758212.
G. Chiarion, L. Sparacino, Y. Antonacci, L. Faes, and L. Mesin, “Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends,” Bioengineering, vol. 10, no. 3, pp. 372–372, Mar. 2023, doi: 10.3390/bioengineering10030372.
A. Cacciotti, C. Pappalettera, F. Miraglia, P. M. Rossini, and F. Vecchio, “EEG entropy insights in the context of physiological aging and Alzheimer’s and Parkinson’s diseases: a comprehensive review,” GeroScience, May 2024, doi: 10.1007/s11357-024-01185-1.
L. Abou-Abbas, I. Jemal, K. Henni, Y. Ouakrim, A. Mitiche, and N. Mezghani, “EEG Oscillatory Power and Complexity for Epileptic Seizure Detection,” Applied Sciences, vol. 12, no. 9, pp. 4181–4181, Apr. 2022, doi: 10.3390/app12094181.
A. F. Pérez et al., “Development of RelaxQuest: A Serious EEG-Controlled Game Designed to Promote Relaxation and Self-Regulation with a Potential Focus on ADHD Intervention,” Applied Sciences, vol. 14, no. 23, pp. 11173–11173, Nov. 2024, doi: 10.3390/app142311173.
H. Zhang et al., “The applied principles of EEG analysis methods in neuroscience and clinical neurology,” Military Medical Research, vol. 10, no. 1, p. 67, Dec. 2023, doi: 10.1186/s40779-023-00502-7.
Y. Zhang and H. D. Fill, “TS-GRU: A Stock Gated Recurrent Unit Model Driven via Neuro-Inspired Computation,” Electronics, vol. 13, no. 23, pp. 4659–4659, Nov. 2024, doi: 10.3390/electronics13234659.
X. Kong et al., “Deep learning for time series forecasting: a survey,” International Journal of Machine Learning and Cybernetics, Feb. 2025, doi: 10.1007/s13042-025-02560-w.
D. Rohan, G. P. Reddy, V. Pavan, K. P. Prakash, and C. P. Reddy, “An extensive experimental analysis for heart disease prediction using artificial intelligence techniques,” Scientific Reports, vol. 15, no. 1, Feb. 2025, doi: 10.1038/s41598-025-90530-1.
K. Zhao and X. Guo, “PilotCareTrans Net: an EEG data-driven transformer for pilot health monitoring,” Frontiers in Human Neuroscience, vol. 19, Jan. 2025, doi: 10.3389/fnhum.2025.1503228.
I. Malashin, V. Tynchenko, A. Gantimurov, V. Nelyub, and A. Borodulin, “Applications of Long Short-Term Memory (LSTM) Networks in Polymeric Sciences: A Review,” Polymers, vol. 16, no. 18, pp. 2607–2607, Sep. 2024, doi: 10.3390/polym16182607.
S. M. Al-Selwi et al., “RNN-LSTM: From applications to modeling techniques and beyond—Systematic review,” Journal of King Saud University - Computer and Information Sciences, vol. 36, no. 5, p. 102068, Jun. 2024, doi: 10.1016/j.jksuci.2024.102068.
I. D. Mienye, T. G. Swart, and G. Obaido, “Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications,” Information, vol. 15, no. 9, pp. 517–517, Aug. 2024, doi: 10.3390/info15090517.
X. Wu, C. Shi, and L. Yan, “Driving Attention State Detection Based on GRU-EEGNet,” Sensors, vol. 24, no. 16, p. 5086, Aug. 2024, doi: 10.3390/s24165086.
C. Jiang et al., “EEG Emotion Recognition Employing RGPCN-BiGRUAM: ReliefF-Based Graph Pooling Convolutional Network and BiGRU Attention Mechanism,” Electronics, vol. 13, no. 13, pp. 2530–2530, Jun. 2024, doi: 10.3390/electronics13132530.
P. B. Weerakody, K. W. Wong, and G. Wang, “Cyclic Gate Recurrent Neural Networks for Time Series Data with Missing Values,” Neural Processing Letters, vol. 55, no. 2, pp. 1527-1554, Jul. 2022, doi: 10.1007/s11063-022-10950-2.
X. Wu, Y. Zhang, J. Li, H. Yang, and X. Wu, “FC-TFS-CGRU: A Temporal–Frequency–Spatial Electroencephalography Emotion Recognition Model Based on Functional Connectivity and a Convolutional Gated Recurrent Unit Hybrid Architecture,” Sensors, vol. 24, no. 6, pp. 1979–1979, Mar. 2024, doi: 10.3390/s24061979.
M. Çelebi, S. Öztürk, and K. Kaplan, “An emotion recognition method based on EWT-3D–CNN–BiLSTM-GRU-AT model,” Computers in Biology and Medicine, vol. 169, pp. 107954–107954, Feb. 2024, doi: 10.1016/j.compbiomed.2024.107954.
W. Jiang et al., “Fuzzy ensemble-based federated learning for EEG-based emotion recognition in Internet of Medical Things,” Journal of Industrial Information Integration, pp. 100789–100789, Feb. 2025, doi: 10.1016/j.jii.2025.100789.
R. Pillalamarri and U. Shanmugam, “A review on EEG-based multimodal learning for emotion recognition,” Artificial Intelligence Review, vol. 58, no. 5, Feb. 2025, doi: 10.1007/s10462-025-11126-9.
E. P. Torres, E. A. Torres, M. Hernández-Álvarez, and S. G. Yoo, “EEG-Based BCI Emotion Recognition: A Survey,” Sensors, vol. 20, no. 18, p. 5083, Sep. 2020, doi: 10.3390/s20185083.
S. A. Hicks et al., “On evaluation metrics for medical applications of artificial intelligence,” Scientific Reports, vol. 12, no. 1, p. 5979, Apr. 2022, doi: 10.1038/s41598-022-09954-8.
T. F. Monaghan et al., “Foundational Statistical Principles in Medical Research: Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value,” Medicina, vol. 57, no. 5, p. 503, May 2021, doi: 10.3390/medicina57050503.
I. M. De Diego, A. R. Redondo, R. R. Fernández, J. Navarro, and J. M. Moguerza, “General Performance Score for classification problems,” Applied Intelligence, vol. 52, no. 10, pp. 12049-12063, Jan. 2022, doi: 10.1007/s10489-021-03041-7.
A. E. Maxwell, T. A. Warner, and L. A. Guillén, “Accuracy Assessment in Convolutional Neural Network-Based Deep Learning Remote Sensing Studies—Part 1: Literature Review,” Remote Sensing, vol. 13, no. 13, p. 2450, Jun. 2021, doi: 10.3390/rs13132450.
Z. He et al., “Unified Convolutional Sparse Transformer for Disease Diagnosis, Monitoring, Drug Development, and Therapeutic Effect Prediction from EEG Raw Data,” Biology, vol. 13, no. 4, pp. 203–203, Mar. 2024, doi: 10.3390/biology13040203.
G. Brookshire et al., “Data leakage in deep learning studies of translational EEG,” Frontiers in Neuroscience, vol. 18, May 2024, doi: 10.3389/fnins.2024.1373515.
M. Bouchane, W. Guo, and S. Yang, “Hybrid CNN-GRU Models for Improved EEG Motor Imagery Classification,” Sensors, vol. 25, no. 5, pp. 1399–1399, Feb. 2025, doi: 10.3390/s25051399.
J. Pachouly, S. Ahirrao, K. Kotecha, G. Selvachandran, and A. Abraham, “A systematic literature review on software defect prediction using artificial intelligence: Datasets, Data Validation Methods, Approaches, and Tools,” Engineering Applications of Artificial Intelligence, vol. 111, no. 1, p. 104773, May 2022, doi: 10.1016/j.engappai.2022.104773.
C. Aliferis and G. Simon, “Overfitting, Underfitting and General Model Overconfidence and Under-Performance Pitfalls and Best Practices in Machine Learning and AI,” Computers in health care (New York), pp. 477–524, Jan. 2024, doi: 10.1007/978-3-031-39355-6_10.
Y. Ma et al., “Emotion Recognition Model of EEG Signals Based on Double Attention Mechanism,” Brain Sciences, vol. 14, no. 12, pp. 1289–1289, Dec. 2024, doi: 10.3390/brainsci14121289.
T. Wang, X. Huang, Z. Xiao, W. Cai, and Y. Tai, “EEG emotion recognition based on differential entropy feature matrix through 2D-CNN-LSTM network,” EURASIP Journal on Advances in Signal Processing, vol. 2024, no. 1, Apr. 2024, doi: 10.1186/s13634-024-01146-y.
X. Wang, Y. Ren, Z. Luo, W. He, J. Hong, and Y. Huang, “Deep learning-based EEG emotion recognition: Current trends and future perspectives,” Frontiers in Psychology, vol. 14, Feb. 2023, doi: 10.3389/fpsyg.2023.1126994.
S. E. Güler and F. P. Akbulut, “Multimodal Emotion Recognition: Emotion Classification through the Integration of EEG and Facial Expressions,” IEEE Access, vol. 13, pp. 24587-24603, Jan. 2025, doi: 10.1109/access.2025.3538642.
H. Torkey, S. Hashish, S. Souissi, E. E.-D. Hemdan, and A. Sayed, “Seizure Detection in Medical IoT: Hybrid CNN-LSTM-GRU Model with Data Balancing and XAI Integration,” Algorithms, vol. 18, no. 2, pp. 77–77, Feb. 2025, doi: 10.3390/a18020077.
M. Khalid et al., “Diagnosing epileptic seizures using combined features from independent components and prediction probability from EEG data,” Digital Health, vol. 10, Jan. 2024, doi: 10.1177/20552076241277185.
M. A. Afzal, Z. Gu, S. U. Bukhari, and B. Afzal, “Brainwaves in the Cloud: Cognitive Workload Monitoring Using Deep Gated Neural Network and Industrial Internet of Things,” Applied Sciences, vol. 14, no. 13, pp. 5830–5830, Jul. 2024, doi: 10.3390/app14135830.
M. Waqas and U. W. Humphries, “A Critical Review of RNN and LSTM Variants in Hydrological Time Series Predictions,” MethodsX, vol. 13, pp. 102946–102946, Sep. 2024, doi: 10.1016/j.mex.2024.102946.
I. H. Sarker, “Deep Learning: a Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions,” SN Computer Science, vol. 2, no. 6, Aug. 2021, doi: 10.1007/s42979-021-00815-1.
F. Rivas, J. E. S.-Garcia, and J. M. Camara, “Comparison of LSTM- and GRU-Type RNN Networks for Attention and Meditation Prediction on Raw EEG Data from Low-Cost Headsets,” Electronics, vol. 14, no. 4, pp. 707–707, Feb. 2025, doi: 10.3390/electronics14040707.
A. Glenn, P. LaCasse, and B. Cox, “Emotion classification of Indonesian Tweets using Bidirectional LSTM,” Neural Computing and Applications, vol. 35, no. 13, pp. 9567-9578, Feb. 2023, doi: 10.1007/s00521-022-08186-1.
M. K. Chowdary, J. Anitha, and D. J. Hemanth, “Emotion Recognition from EEG Signals Using Recurrent Neural Networks,” Electronics, vol. 11, no. 15, p. 2387, Jul. 2022, doi: 10.3390/electronics11152387.
A. Abgeena and S. Garg, “S-LSTM-ATT: a hybrid deep learning approach with optimized features for emotion recognition in electroencephalogram,” Health Information Science and Systems, vol. 11, no. 1, Aug. 2023, doi: 10.1007/s13755-023-00242-x.
J. Z. Wang et al., “Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion,” Proceedings of the IEEE, vol. 111, no. 10, pp. 1–51, 2023, doi: 10.1109/JPROC.2023.3273517.
K. L. Lew, K. S. Sim, and Z. Ting, “Deep Learning Approach EEG Signal Classification,” JOIV: International Journal on Informatics Visualization, vol. 8, no. 3–2, p. 1693, Nov. 2024, doi: 10.62527/joiv.8.3-2.2959.
A. D. Wibawa, B. S. Y. Mohammad, M. A. K. Fata, F. A. Nuraini, A. Prasetyo, and Y. Pamungkas, “Comparison of EEG-Based Biometrics System Using Naive Bayes, Neural Network, and Support Vector Machine,” 2022 International Conference on Electrical and Information Technology (IEIT), pp. 408-413, Sep. 2022, doi: 10.1109/ieit56384.2022.9967861.
I. H. Sarker, “Machine Learning: Algorithms, Real-World Applications and Research Directions,” SN Computer Science, vol. 2, no. 3, pp. 1–21, 2021, doi: 10.1007/s42979-021-00592-x.
S. S. Prasetiyowati and Y. Sibaroni, “Unlocking the potential of Naive Bayes for spatio temporal classification: a novel approach to feature expansion,” Journal of Big Data, vol. 11, no. 1, Aug. 2024, doi: 10.1186/s40537-024-00958-x.
M.-A. Schulz et al., “Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets,” Nature Communications, vol. 11, no. 1, 2020, doi: 10.1038/s41467-020-18037-z.
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