Electroencephalography (EEG) eye state classification using learning vector quantization and bagged trees

Mehrbakhsh Nilashi; Rabab Ali Abumalloh; Hossein Ahmadi; Sarminah Samad; Abdullah Alghamdi; Mesfer Alrizq; Sultan Alyami; Fatima Khan Nayer

doi:10.1016/j.heliyon.2023.e15258

Electroencephalography (EEG) eye state classification using learning vector quantization and bagged trees

Heliyon. 2023 Apr 5;9(4):e15258. doi: 10.1016/j.heliyon.2023.e15258. eCollection 2023 Apr.

Authors

Mehrbakhsh Nilashi^{1

2}, Rabab Ali Abumalloh³, Hossein Ahmadi⁴, Sarminah Samad⁵, Abdullah Alghamdi⁶, Mesfer Alrizq⁶, Sultan Alyami⁷, Fatima Khan Nayer⁸

Affiliations

¹ UCSI Graduate Business School, UCSI University, No. 1 Jalan Menara Gading, UCSI Heights, 56000, Cheras, Kuala Lumpur, Malaysia.
² Centre for Global Sustainability Studies (CGSS), Universiti Sains Malaysia, 11800 Penang, Malaysia.
³ Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar.
⁴ Centre for Health Technology, Faculty of Health, University of Plymouth, Plymouth PL4 8AA, UK.
⁵ Department of Business Administration, College of Business and Administration, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
⁶ Information Systems Department, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia.
⁷ Computer Science Department, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia.
⁸ Artificial Intelligence and Data Analytics (AIDA) Research Lab, College of Computer and Information Sciences, Prince Sultan University, Saudi Arabia.

Abstract

The analysis of Electroencephalography (EEG) signals has been an effective way of eye state identification. Its significance is highlighted by studies that examined the classification of eye states using machine learning techniques. In previous studies, supervised learning techniques have been widely used in EEG signals analysis for eye state classification. Their main goal has been the improvement of classification accuracy through the use of novel algorithms. The trade-off between classification accuracy and computation complexity is an important task in EEG signals analysis. In this paper, a hybrid method that can handle multivariate signals and non-linear is proposed with supervised and un-supervised learning to achieve a fast EEG eye state classification with high prediction accuracy to provide real-time decision-making applicability. We use the Learning Vector Quantization (LVQ) technique and bagged tree techniques. The method was evaluated on a real-world EEG dataset which included 14976 instances after the removal of outlier instances. Using LVQ, 8 clusters were generated from the data. The bagged tree was applied on 8 clusters and compared with other classifiers. Our experiments revealed that LVQ combined with the bagged tree provides the best results (Accuracy = 0.9431) compared with the bagged tree, CART (Classification And Regression Tree) (Accuracy = 0.8200), LDA (Linear Discriminant Analysis) (Accuracy = 0.7931), Random Trees (Accuracy = 0.8311), Naïve Bayes (Accuracy = 0.8331) and Multilayer Perceptron (Accuracy = 0.7718), which demonstrates the effectiveness of incorporating ensemble learning and clustering approaches in the analysis of EEG signals. We also provided the time complexity of the methods for prediction speed (Observation/Second). The result showed that LVQ + Bagged Tree provides the best result for prediction speed (58942 Obs/Sec) in relation to Bagged Tree (28453 Obs/Sec), CART (27784 Obs/Sec), LDA (26435 Obs/Sec), Random Trees (27921), Naïve Bayes (27217) and Multilayer Perceptron (24163).

Keywords: Bagged trees; Electroencephalography; Eye state classification; Human cognition state; Learning vector quantization.