A Novel Real-Time EEG Based Eye State Recognition System

  • Zijia Zhou
  • Pan LiEmail author
  • Jianqi Liu
  • Weikuo Dong
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 262)


With the development of brain-computer interface (BCI) technology, fast and accurate analysis of Electroencephalography (EEG) signals becomes possible and has attracted a lot of attention. One of the emerging applications is eye state recognition based on EEG signals. A few schemes like the K* algorithm have been proposed which can achieve high accuracy. Unfortunately, they are generally complex and hence too slow to be used in a real-time BCI framework such as an instance-based learner. In this paper, we develop a novel effective and efficient EEG based eye state recognition system. The proposed system consists of four parts: EEG signal preprocessing, feature extraction, feature selection and classification. First, we use the ‘sym8’ wavelet to decompose the original EEG signal and select the 5th floor decomposition, which is subsequently de-noised by the heuristic SURE threshold method. Then, we propose a novel feature extraction method by utilizing the information accumulation algorithm based on wavelet transform. By using the CfsSubsetEval evaluator based on the BestFirst search method for feature selection, we identify the optimal features, i.e., optimal scalp electrode positions with high correlations to eye states. Finally, we adopt Random Forest as the classifier. Experiment results show that the accuracy of the overall EEG eye state recognition system can reach 99.8% and the minimum number of training samples can be kept small.


Electroencephalogram (EEG) Eye state identification Feature extraction Wavelet transform Information accumulation algorithm Random forest 


  1. 1.
    Kim, Y.-J., Kwak, N.-S., Lee, S.-W.: Classification of motor imagery for Ear-EEG based brain-computer interface. In: 2018 6th International Conference on Brain-Computer Interface (BCI). IEEE (2018)Google Scholar
  2. 2.
    Hailin, W., Hanhui, L., Zhumei, S.: Driving detection system design based on driving behavior. In: 2010 International Conference on Optoelectronics and Image Processing. IEEE (2010)Google Scholar
  3. 3.
    Sinha, A.K., Loparo, K.A., Richoux, W.J.: A new system theoretic classifier for detection and prediction of epileptic seizures. In: The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE (2005)Google Scholar
  4. 4.
    Fraiwan, L., Lweesy, K.: Neonatal sleep state identification using deep learning autoencoders. In: 2017 IEEE 13th International Colloquium on Signal Processing and its Applications (CSPA). IEEE (2017)Google Scholar
  5. 5.
    Leow, A., et al.: Measuring inter-hemispheric integration in bipolar affective disorder using brain network analyses and HARDIA. In: 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI) In 4th International Conference. IEEE, 2012Google Scholar
  6. 6.
    RoSler, O., Suendermann, D.: A first step towards eye state prediction using EEG. In: AIHLS 2013: The International Conference on Applied Informatics for Health and Life Sciences. IEEE (2013)Google Scholar
  7. 7.
    Hamilton, C.R., Shahryari, S., RasheedEye, K.M.: State prediction from EEG data using boosted rotational forests, In: IEEE International Conference on Machine Learning. IEEE (2016)Google Scholar
  8. 8.
    Reddy, T.K., Behera, L.: Online eye state recognition from EEG data using deep architectures.: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016). IEEE (2016)Google Scholar
  9. 9.
    Girault, B., Gonc¸alves, P., Fleury, E., Mor, A.S.: Semisupervised learning for graph to signal mapping: A graph signal wiener filter interpretation. In: Proceedings of IEEE International Conference Acoustic, Speech and Signal Process. (ICASSP), pp.1115–1119. IEEE (2014)Google Scholar
  10. 10.
    Huang, T.S. (ed.): Two-Dimensional Digital Signal Processing II: Transforms and Median Filters. Springer-Verlag, Berlin (1981)zbMATHGoogle Scholar
  11. 11.
    Grossmann, A.: Wavelet transforms and edge detection to be published in Stochastic Processes in Physics and Engineering, Ph.Blanchard, L. Streit, and M. Hasewinkel, EdsGoogle Scholar
  12. 12.
    Jin, Z., Jia-lunl, L., Xiao-ling, L., wei-quan, W.: ECG signals denoising method based on improved wavelet threshold algorithm. In: Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). IEEE (2016)Google Scholar
  13. 13.
    Saxena, S., Khanduga, H.S., Mantri, S.: An efficient denoising method based on SURE-LET and Wavelet Transform. In: International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). IEEE (2016)Google Scholar
  14. 14.
    Movahednasab, M., Soleimanifar, M., Gohari, A.: Adaptive transmission rate with a fixed threshold decoder for diffusion-based molecular communication. IEEE Trans. Commun. 64, 236–248 (2016)CrossRefGoogle Scholar
  15. 15.
    Wang, Y., He, S., Jiang, Z.: Weak GNSS signal acquisition based on wavelet de-noising through lifting scheme and heuristic threshold optimization.In: 2014 International Symposium on Wireless Personal Multimedia Communications (WPMC). IEEE (2015)Google Scholar
  16. 16.
    Zhifeng Lin, Zhihua Huang”. Research on event-related potentials in motor imagery BCI”: 10th International Congress on Image and Signal Processing, p. 2017. BioMedical Engineering and Informatics (CISP-BMEI), IEEE (2017)Google Scholar
  17. 17.
    Kidmose, P., Looney, D., Ungstrup, M., Rank, M.L., MandicA, D.P.: Study of evoked potentials from ear-EEG. IEEE Trans. Biomed. Eng. 60, 2824–2830 (2013)CrossRefGoogle Scholar
  18. 18.
    Saad, S., Ishtiyaque, M., Malik, H.: Selection of most relevant input parameters using WEKA for artificial neural network based concrete compressive strength prediction model. In: Power India International Conference (PIICON). IEEE (2016)Google Scholar
  19. 19.
    Kumar, N., Khatri, S.: Implementing WEKA for medical data classification and early disease prediction. In: 3rd International Conference on Computational Intelligence and Communication Technology (CICT). IEEE (2017)Google Scholar
  20. 20.
    Kooistra, I., Kuilder, E.T., Mücher, C.A.: Object-based random forest classification for mapping floodplain vegetation structure from nation-wide CIR AND LiDAR datasets. In: Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE (2017)Google Scholar
  21. 21.
    Frank, A., Asuncion, A.: UCI Machine Learning Repository. (2010)
  22. 22.
    Diego, S., Toscano, B.S., Silva, A.: On the use of the Emotiv EPOC neuroheadset as a low cost alternative for EEG signal acquisition. In: 2016 IEEE Colombian Conference on Communications and Computing (COLCOM). IEEE (2016)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.University of Electronic Science and Technology of ChinaChengduChina

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