2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 17 , Issue 2 , PP: 211-218, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Mohammed Yousif 1 , Iman Ameer Ahmad 2 , Assef Raad Hmeed 3 , Abdulrahman Abbas Mukhlif 4
Doi: https://doi.org/10.54216/FPA.170216
This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance while minimizing redundancy. This optimization process improves the performance of the classification model in general. In case of classification, the Support Vector Machine (SVM) and Neural Network (NN) hybrid model is presented. This combines an SVM classifier's capacity to manage functions in high dimensional space, as well as a neural network capacity to learn non-linearly with its feature (pattern learning). The model was trained and tested on an EEG dataset and performed a classification accuracy of 97%, indicating the robustness and efficacy of our method. The results indicate that this improved classifier is able to be used in brain–computer interface systems and neurologic evaluations. The combination of machine learning and optimization techniques has established this paradigm as a highly effective way to pursue further research in EEG signal processing for brain language recognition.
Machine learning , Gray wolf optimization , Neural network , Wavelet , EEG , SVM
[1] M. A. Jumaah, A. I. Shihab, and A. A. Farhan, “Epileptic seizures detection using DCT-II and KNN classifier in long-term EEG signals,” Iraqi J. Sci., vol. 61, no. 10, pp. 2687–2694, 2020, doi: 10.24996/ijs.2020.61.10.26.
[2] A. Abdar, et al., “A new nested ensemble technique with feature selection for healthcare diagnosis,” Computers in Biology and Medicine, vol. 113, 2021.
[3] M. Kaya, et al., “Feature selection using binary particle swarm optimization and support vector machines for coronary artery disease detection,” Expert Systems with Applications, vol. 42, no. 2, pp. 1004–1012, 2020.
[4] M. A. Kadhim and A. M. Radhi, “Machine Learning Prediction of Brain Stroke at an Early Stage,” Iraqi J. Sci., vol. 64, no. 12, pp. 6596–6610, 2023, doi: 10.24996/ijs.2023.64.12.39.
[5] F. H. Hassan and M. A. Omar, “Recurrent Stroke Prediction using Machine Learning Algorithms with Clinical Public Datasets: An Empirical Performance Evaluation,” Baghdad Sci. J., vol. 18, no. 4, pp. 1406–1412, 2021, doi: 10.21123/bsj.2021.18.4(Suppl.).1406.
[6] M. M. Dey and K. Murase, “Optimization of EEG feature selection for classification of brain–computer interfaces using wavelet packet transform and genetic algorithm,” Journal of Neuroscience Methods, vol. 295, pp. 47-63, 2021.
[7] G. Prasad, et al., “A systematic review of brain signal feature extraction techniques for BCI applications,” Journal of Neural Engineering, vol. 17, no. 5, pp. 051002, 2020.
[8] A. Subasi, M. Radhwan, M. A. Ismail, and E. Dogdu, “EEG signal classification using wavelet feature extraction and a hybrid MLP-CNN deep learning model,” IEEE Access, vol. 8, pp. 105459-105469, 2020.
[9] Y. Zhang, Y. Xie, F. Jin, and Y. Liang, “An improved wavelet transform and SVM-based classification method for EEG signals,” Journal of Neural Engineering, vol. 18, no. 3, pp. 036016, 2021.
[10] S. Mirjalili, et al., “Grey Wolf Optimizer,” Advances in Engineering Software, vol. 69, pp. 46-61, 2020.
[11] A. Hassan and A. Subasi, “A decision support system for automated identification of sleep stages from single-channel EEG signals,” Knowledge-Based Systems, vol. 128, pp. 115-126, 2022.
[12] S. Bhardwaj, A. Tiwari, and S. Saraswat, “Optimization of EEG feature selection and classification for brain-computer interface using hybrid evolutionary algorithm,” Expert Systems with Applications, vol. 173, pp. 114741, 2021.
[13] J. Liu, X. Li, and Z. Chen, “Gray wolf optimization algorithm for feature selection and EEG signal classification,” Applied Soft Computing, vol. 96, pp. 106645, 2020.
[14] Mohammed, Noor A., et al. "Performance Analysis of Different Machine Learning Algorithms for Predictive Maintenance." Al-Khwarizmi Engineering Journal 20.2 (2024): 26-38.
[15] M. A. Mohammed, B. Al-khateeb, M. Yousif, S. A. Mostafa, S. Kadry, and K. H. Abdulkareem, “Novel Crow Swarm Optimization Algorithm and Selection Approach for Optimal Deep Learning COVID-19 Diagnostic Model,” vol. 2022, 2022.
[16] N. M. Ghadi and N. H. Salman, “Deep Learning-Based Segmentation and Classification Techniques for Brain Tumor MRI: A Review,” J. Eng., vol. 28, no. 12, pp. 93–112, 2022, doi: 10.31026/j.eng.2022.12.07.
[17] Roy, Y., Banville, H., Albuquerque, I., Gramfort, A., Falk, T. H., & Faubert, J. (2019). Deep learning-based electroencephalography analysis: A systematic review. Journal of Neural Engineering, 16(5), 051001.
[18] Wang, L., Li, Y., Zhang, S., et al. (2020). EEG signal classification using wavelet transform and improved SVM optimized by modified crow search algorithm. Biomedical Signal Processing and Control, 58, 101837.
[19] Xie, Y., Chen, L., Yang, H., & Li, G. (2020). PSO-based DBN optimized framework for EEG classification. Neurocomputing, 394, 42-50.
[20] Zhou, S., Guo, Z., & Tang, S. (2021). Optimized SVM with genetic algorithms for motor imagery EEG classification. Pattern Recognition Letters, 140, 17-24.
[21] Xu, Q., Li, J., & Zhang, H. (2022). Hybrid CNN-GA system for EEG signal classification in cognitive tasks. Artificial Intelligence in Medicine, 123, 102192.
[22] Gao, Z., Zhang, Y., Li, J., & Xu, Z. (2021). Hybrid optimization and deep learning for EEG signal classification: A comparative study. IEEE Transactions on Neural Networks and Learning Systems, 32(4), 1810-1820.
