The Classification of Eye State by Using kNN and MLP Classification Models According to the EEG Signals

Yıl 2015, , 127 - 130, 15.12.2015

Murat Koklu , Kadir Sabancı

https://doi.org/10.18201/ijisae.75836

Cited By: 37

Öz

What is widely used for classification of eye state to detect human’s cognition state is electroencephalography (EEG). In this study, the usage of EEG signals for online eye state detection method was proposed. In this study, EEG eye state dataset that is obtained from UCI machine learning repository database was used. Continuous 14 EEG measurements forms the basic of the dataset. The duration of the measurement is 117 seconds (each measurement has14980 sample). Weka (Waikato Environment for Knowledge Analysis) program is used for classification of eye state. Classification success was calculated by using k-Nearest Neighbors algorithm and multilayer perceptron neural networks models. The obtained success of classification methods were compared. The classification success rates were calculated for various number of neurons in the hidden layer of a multilayer perceptron neural network model. The highest classification success rate have been obtained when the number of neurons in the hidden layer was equal to 7. And it was 56.45%. The classification success rates were calculated with k-nearest neighbors algorithm for different neighbourhood values. The highest success was achieved in the classification made with kNN algorithm.  In kNN models, the success rate for 3 nearest neighbor were calculated as 84.05%.

Anahtar Kelimeler

EEG signals, eye state, weka, multilayer perceptron, kNN classifier

Kaynakça

• T. C. Sharma and M. Jain, “WEKA Approach for Comparative Study of Classification Algorithm”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 4, April 2013
• O. Roesler and D. Suendermann, “A First Step towards Eye State Prediction Using EEG”. In Proc. of the AIHLS 2013, International Conference on Applied Informatics for Health and Life Sciences, Istanbul, Turkey, September 2013
• T. Wang, S. U. Guan, K. L. Man and T. O. Ting, “EEG eye state identification using incremental attribute learning with time-series classification”. Mathematical Problems in Engineering, 2014
• O. Fukuda, T. Tsuji, and M. Kaneko, “Pattern classification of EEG signals using a log-linearized Gaussian mixture neural network,” inProceedings of the IEEE International Conference on Neural Networks. Part 1 (of 6), pp. 2479–2484, Perth, Australia, December 1995.
• M. V. M. Yeo, X. Li, K. Shen, and E. P. V. Wilder-Smith, “Can SVM be used for automatic EEG detection of drowsiness during car driving?” Safety Science, vol. 47, no. 1, pp. 115–124, 2009.
• K. Polat and S. Güneş, “Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform,” Applied Mathematics and Computation, vol. 187, no. 2, pp. 327–1026, 2007.
• N. Sulaiman, M. N. Taib, S. Lias, Z. H. Murat, S. A. M. Aris, and N. H. A. Hamid, “Novel methods for stress features identification using EEG signals,” International Journal of Simulation: Systems, Science and Technology, vol. 12, no. 1, pp. 27–33, 2011.
• M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, “The WEKA data mining software: an update,” ACM SIGKDD Explorations Newsletter, vol. 11, pp. 10–18, 2009.
• A. Frank and A. Asuncion, “UCI machine learning repository”, http://archive.ics.uci.edu/ml. 2014.
• T. Wang, S.U. Guan, K.L Man, and T. O. Ting, “EEG Eye State Identification Using Incremental Attribute Learning with Time-Series Classification”, Hindawi Publishing Corporation, Mathematical Problems in Engineering, Volume 2014, Article ID 36532, 9 pages
• M. Sahu, N. K. Nagwani, S. Verma, and S.Shirke, “An Incremental Feature Reordering (IFR) Algorithm to Classify Eye State Identification Using EEG”, Information Systems Design and Intelligent Applications, Advances in Intelligent Systems and Computing Volume 339, 2015, pp 803-811
• O. Roesler, L. Bader, J. Forster, Y. Hayashi, S. Hessler, and D. Suendermann-Oeft, “Comparison of EEG Devices for Eye State Classification”. In Proc. of the AIHLS 2014, International Conference on Applied Informatics for Health and Life Sciences, Kusadasi, Turkey, October 2014.
• K. Hassani and W.S. Lee, “An Incremental Framework for Classification of EEG Signals Using Quantum Particle Swarm Optimization”, Conference: 2014 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) DOI: 10.1109/CIVEMSA.2014.6841436
• R. Singla, B. Chambayil, A. Khosla, J, and Santosh, “Comparison of SVM and ANN for classification of eye events in EEG”, J. Biomedical Science and Engineering, 2011, 4, 62-69, doi:10.4236/jbise.2011.41008.
• T. Razzaghi, O. Roderick, I. Safro, and N. Marko, “ Fast Imbalanced Classification of Healthcare Data with Missing Values” . 2015 arXiv preprint arXiv:1503.06250.
• O. Roesler, it12148 '@' lehre.dhbw-stuttgart.de, Baden-Wuerttemberg Cooperative State University (DHBW), Stuttgart, Germany.
• WEKA, http://www.cs.waikato.ac.nz/~ml/weka/ Last access: 10.04.2015.
• R. Arora and Suman, “Comparative Analysis of Classification Algorithms on Different Datasets using WEKA”, International Journal of Computer Applications (0975–8887) Volume 54– No.13, September 2012.
• J. Wang, P. Neskovic, and L. N. Cooper, “Improving nearest neighbor rule with a simple adaptive distance measure”, Pattern Recognition Letters, 28(2):207-213, 2007
• Y. Zhou, Y. Li, and S. Xia, “An improved KNN text classification algorithm based on clustering”, Journal of computers, 4(3):230-237, 2009.