Eye State Classification from Electroencephalography (EEG) Signals Using the Extra Trees Classifier Algorithm

Öz

This study aims to automatically classify the eye openness state (open/closed) of individuals from electroencephalography (EEG) signals. In the classification process, based on the knowledge that EEG signals reflect short-term cognitive states, the EEG Eye State dataset is used. The dataset contains 14,980 samples from 14 EEG channels and the eye state is labelled according to the binary classification problem. Within the scope of the preprocessing steps for the data, the scaling process was performed and then the classification model was created. In the modelling process, the Extra Trees Classifier (ETC) algorithm, which is an ensemble learning method based on decision trees, was preferred. The performance of the model was evaluated by 10-fold cross-validation method; accuracy, precision, sensitivity and F1 score metrics were calculated at each layer. The findings revealed that the model performed well in all metrics. In particular, the highest F1 score was achieved in Fold 1, and the width of the area under the ROC curve (AUC) confirmed the discriminative power of the model. In addition, in the feature importance analysis, it was observed that the signals obtained from occipital and parietal regions contributed more to the classification process. The results show that traditional machine learning algorithms, together with appropriate preprocessing strategies, can produce effective classification outputs on EEG data. This study contributes to the academic literature on EEG-based eye state detection and provides a meaningful basis for applications such as human-computer interaction, attention monitoring systems and neurocognitive assessment.

Anahtar Kelimeler

• Electroencephalography
• Extra Trees
• Classifier
• Eye State Machine Learning

Kaynakça

1. [1] Piatek, Ł., Fiedler, P., & Haueisen, J. (2018). Eye state classification from electroencephalography recordings using machine learning algorithms. Digital Medicine, 4(2), 84-95.
2. [2] Bharati, S., Podder, P., & Raihan-Al-Masud, M. (2018, November). EEG eye state prediction and classification in order to investigate human cognitive state. In 2018 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE) (pp. 1-4). IEEE.
3. [3] Nilashi, M., Abumalloh, R. A., Ahmadi, H., Samad, S., Alghamdi, A., Alrizq, M., ... & Nayer, F. K. (2023). Electroencephalography (EEG) eye state classification using learning vector quantization and bagged trees. Heliyon, 9(4).
4. [4] Ketu, S., & Mishra, P. K. (2022). Hybrid classification model for eye state detection using electroencephalogram signals. Cognitive Neurodynamics, 16(1), 73-90.
5. [5] Saeid Sanei, Jonathon A. Chambers, “ EEG Signal Processing,” John Wiley & Sons, 2013.
6. [6] Peter Wolf (M.D.), “Epileptic Seizures and Syndromes: With Some of Their Theoretical Implications,” John Libbey Eurotext, 1994.
7. [7] Aleksandar Čolić, Oge Marques, Borko Furht, “Driver Drowsiness Detection: Systems and Solutions,” Springer, 2014.
8. [8] Mardi Z, Ashtiani SNM, Mikaili M (2011) EEG-based drowsiness detection for safe driving using chaotic features and statistical tests. J Med Signals Sens 1(2):130

9. [9] Khurram I.Qazia, H.K.Lama, BoXiao, Gaoxiang Ouyang, XunheYinc, “Classification of epilepsy using computational intelligence techniques”, CAAI Transactions on Intelligence Technology, Volume 1, Issue 2, Pages 137-149, April 2016.
10. [10] Correa AG, Orosco L, Laciar E (2014) Automatic detection of drowsiness in EEG records based on multimodal analysis. Med Eng Phys 36(2):244–249.
11. [11] Sanei, S., & Chambers, J. A. (2013). EEG signal processing. John Wiley & Sons.
12. [12] Laport, F., Castro, P. M., Dapena, A., Vazquez-Araujo, F. J., & Iglesia, D. (2020, August). Study of machine learning techniques for eeg eye state detection. In Proceedings (Vol. 54, No. 1, p. 53). MDPI.
13. [13] Mumtaz W, Ali SSA, Yasin MAM, Malik AS (2018) A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD). Med Biol Eng Comput 56(2):233–246
14. [14] Zhang T, Chen W, Li M (2019) Classification of inter-ictal and ictal EEGs using multi-basis MODWPT, dimensionality reduction algorithms and LS-SVM: a comparative study. Biomed Signal Process Control 47:240–251.
15. [15] Chatterjee R, Maitra T, Islam SH, Hassan MM, Alamri A, Fortino G (2019a) A novel machine learning based feature selection for motor imagery EEG signal classification in Internet of medical things environment. Future Gener Comput Syst 98:419–434
16. [16] Gajbhiye, P., Singh, V., Saharan, S., & Joshi, S. (2024). Artificial neural network-based classification of eye states using electroencephalogram signals: a comparative analysis of algorithms and artifact removal techniques. In Artificial Intelligence: A tool for effective diagnostics (pp. 5-1). Bristol, UK: IOP Publishing.
17. [17] O. Roesler, “UCI Machine Learning Repository [EEG Eye State],” Baden-Wuerttemberg Cooperative State University (DHBW), Stuttgart, Germany, 2013. [Online]. Available: https://archive.ics.uci.edu/ml/datasets/EEG+Eye+State
18. [18] Keerthi Latha, C. V., & Kezia Joseph, M. (2024, June). Enhancing Depression Detection Using EEG Signals Through Adaptive Feature Weighting in Extra Trees Classifier. In International Conference on Frontiers of Intelligent Computing: Theory and Applications (pp. 583-592). Singapore: Springer Nature Singapore.
19. [19] Shafique, R., Mehmood, A., & Choi, G. S. (2019). Cardiovascular disease prediction system using extra trees classifier. Res. Sq, 11, 51.
20. [20] Hassan, M. M., Hassan, M. R., Huda, S., Uddin, M. Z., Gumaei, A., & Alsanad, A. (2021). A predictive intelligence approach to classify brain–computer interface based eye state for smart living. Applied Soft Computing, 108, 107453.
21. [21] Jayadurga, N. P., Chandralekha, M., & Saleem, K. (2024, May). Leveraging Ensemble Techniques for Enhanced Eye State Classification with EEG Data. In 2024 2nd International Conference on Advancement in Computation & Computer Technologies (InCACCT) (pp. 570-576). IEEE.
22. [22] Xiao, Q., Yang, M., & Yuan, K. (2023). Eye state recognition based on continuous wavelet transform and improved convolutional neural network.
23. [23] Adil, S. H., Ebrahim, M., Raza, K., & Ali, S. S. A. (2018, August). Prediction of eye state using knn algorithm. In 2018 International Conference on Intelligent and Advanced System (ICIAS) (pp. 1-5). IEEE.
24. [24] Fikri, M. A., Santosa, P. I., & Wibirama, S. (2021, October). A review on opportunities and challenges of machine learning and deep learning for eye movements classification. In 2021 IEEE International Biomedical Instrumentation and Technology Conference (IBITeC) (pp. 65-70). IEEE.
25. [25] Alkhaldi, M., Joudeh, L. A., Ahmed, Y. B., & Husari, K. S. (2024). Artificial intelligence and telemedicine in epilepsy and EEG: A narrative review. Seizure: European Journal of Epilepsy, 121, 204-210.
26. [26] Gaddanakeri, R. D., Naik, M. M., Kulkarni, S., & Patil, P. (2024, April). Analysis of EEG signals in the DEAP dataset for emotion recognition using deep learning algortihms. In 2024 IEEE 9th International Conference for Convergence in Technology (I2CT) (pp. 1-7). IEEE.
27. [27] Yu, W. Y., Sun, T. H., Hsu, K. C., Wang, C. C., Chien, S. Y., Tsai, C. H., & Yang, Y. W. (2024). Comparative analysis of machine learning algorithms for Alzheimer's disease classification using EEG signals and genetic information. Computers in Biology and Medicine, 176, 108621.
28. [28] Hazar, Y., & Ertuğrul, Ö. F. (2025). Process management in diabetes treatment by blending technique. Computers in Biology and Medicine, 190, 110034.
29. [29] Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine learning, 63, 3-42.
30. [30] Hussein, N., & Zeebaree, S. R. (2024). Performance Evaluation of Extra Trees Classifier by using CPU Parallel and Non-Parallel Processing. The Indonesian Journal of Computer Science, 13(2).
31. [31] A. G´eron, Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, O’Reilly Media, Inc, 2022.
32. [32] M.A. Jamil, S. Khanam, Influence of One-Way ANOVA and Kruskal–Wallis Based Feature Ranking on the Performance of ML Classifiers for Bearing Fault Diagnosis, Journal of Vibration Engineering & Technologies, 2023, pp. 1–32.
33. [33] A. Al Imran, A. Rahman, H. Kabir, M.S. Rahim, The impact of feature selection techniques on the performance of predicting Parkinson’s disease, Int. J. Inf. Technol. Comput. Sci. 10 (11) (2018) 14–29.
34. [34] N. Silpa, V.M. Rao, M.V. Subbarao, R.R. Kurada, S.S. Reddy, P.J. Uppalapati, An enriched employee retention analysis system with a combination strategy of feature selection and machine learning techniques, in: 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS), IEEE, 2023, May, pp. 142–149.