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Hybrid EEG-Based Eye State Classification Using LSTM, Neural Networks, and Multivariate Analysis | ||
| Journal of Information Technology Management | ||
| دوره 17، Special Issue on SI: Intelligent Security and Management، 2025، صفحه 16-31 اصل مقاله (1.51 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jitm.2025.102919 | ||
| نویسندگان | ||
| Kaku Riya Dharmendra1؛ Deepak Kumar Verma* 2؛ Satvik Vats3 | ||
| 1Ph.D., Department of Computer Engineering, Marwadi University, Rajkot, Gujarat-360003. | ||
| 2Department of Computer Engineering, Marwadi University, Rajkot, Gujarat- 360003. | ||
| 3Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, U.P., India- 273010. | ||
| چکیده | ||
| This paper focuses on a new hybrid machine learning model for classifying eye states from EEG signals by integrating traditional techniques with deep learning methods. Our Hybrid LSTM-KNN architecture employs KNN for classification and uses LSTM networks to extract features temporally. In addition, we perform extensive feature engineering, including statistical Z-test and IQR filtering, dimensionality reduction using PCA, and multivariate analysis to further model the performance. Moreover, an SVM-based unsupervised clustering approach is proposed to partition the EEG feature space, followed by ensemble learning in each cluster to improve accuracy and robustness. Using the EEG Eye State Dataset for the first assessment, the Hybrid LSTM-KNN model recorded an accuracy of 87.2% without PCA. Further improvements through statistical filtering outperformed initial expectations, achieving a 6% rise in performance to 89.1% after outlier removal, 89.1% with Z-test (σ = 3), and 88.3% with IQR (1.5x). After applying PCA along with ensemble learning post clustering, the final model exceeded expectations with an accuracy and F1 score of 96.8%, surpassing Ensemble Cluster-KNN and traditional models based on Ensemble Cluster-KNN, Logistic Regression, SVM, and Random Forest. The outcome demonstrates the robustness and noise-resilience of the model’s performance in practical real-time brain-computer interface and cognitive monitoring systems. | ||
| کلیدواژهها | ||
| Machine learning؛ Ensemble classifiers؛ Feature Selection؛ SVM؛ LSTM | ||
| مراجع | ||
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