Eye State Prediction Based on EEG Signal Data Neural Network and Evolutionary Algorithm Optimization

  • Untari Novia Wisesty Telkom University
  • Hifzi Priabdi Telkom University
  • Rita Rismala Telkom University
  • Mahmud Dwi Sulistiyo Telkom University
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Eye state prediction is one study using EEG signals obtained to predict the state of the human eye several moments before. In its development, many researchers also have built eye states detection schemes, but the system built is only limited to classifying one record of input data obtained from the Emotive EPOC headset channel into the eye state. Therefore, this paper proposed eye state prediction system where the system can predict the state of the human eye some time previously based on the EEG signal series used. The proposed system consists of two parts, namely the prediction of the EEG signal value and eye state detection based on the value of the signal that has been obtained using Differential Evolution and Neural Network optimized by Evolution Strategies, respectively. The highest accuracy obtained from the eye state prediction system that has been built is 73.2%. These results are obtained by the best combination of parameters from the three methods used.


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Jain, Neha, S. Bhargaya, S. Shivani, D. Goyal. Eye State Prediction Using EEG by Supervised Learning. International Journal of Science, Engineering, and Technology. 2015.

Devipriya A, N. Nagarajan, Brindha D. Expert System based Machine Learning Techniques for Eye State Prediction. International Journal of Pure and Applied Mathematics. 2018; Vol 110: No. 12, pp. 1173-1186.

Rösler, O. and Suendermann, D. A First Step Towards Eye State Prediction using EEG. Proc. of the AIHLS. 2013.

Chen, L., L. Li, and L. Xiao. Differences of EEG between Eyes-Open and Eyes-Closed States Based on Autoregressive Method. Journal of Electronic Science and Technology of China. 2009; 175-179.

Sabanci, Kadir, Murat Koklu. The Classification of Eye State by Using KNN and MLP Classification Model According to the EEG Signal. International Journal of Intelligent System and Applications in Engineering. 2015; Vol. 3: pp:127-130.

Rosler, Oliver, Lucas Bader, Jan Forster, Yoshikatsu Hayashi, Stefan Hebler, David Suendermann-Oeft. Comparison of EEG Devices for Eye State Classification. Proc. of the AIHLS 2014, Istanbul Turkey.

Wisesty, Untari N. Levenberg-Marquardt Neural Network for Eye States Detection Based on Electroencephalography Data. International Journal on Information and Communication Technology (IJoICT). 2016; Vol 2(1): 23-36.

Prakoso, Ersa Christian, Untari Novia Wisesty, Jondri. Klasifikasi Keadaan Mata Berdasarkan sinyal EEG menggunakan Extreme Learning Machines. Indonesian Journal on Computing. 2016; Vol. 1: Issue. 2, pp. 97-116.

Wang, T., Guan, S. U., Man, K. L., & Ting, T. O. Time Series Classification for EEG Eye State Identification based on Incremental Attribute Learning. International Symposium on Computer, Consumer and Control (IS3C). 2014; pp. 158-161.

Sahu, Mridu, N. K. Nagwani, S. Verma, S. Shirke. Performance Evaluation of Difference Classifier for Eye State Prediction using EEG Signal. International Journal of Knowledge Engineering. 2015; Vol 1:2.

Suyanto. Evolutionary Computation Komputasi Berbasis ”Evolusi” dan “Genetika”. Bandung: Informatika. 2008.

How to Cite
Wisesty, U. N., Priabdi, H., Rismala, R., & Sulistiyo, M. D. (2020). Eye State Prediction Based on EEG Signal Data Neural Network and Evolutionary Algorithm Optimization. Indonesia Journal on Computing (Indo-JC), 5(1), 33-44. https://doi.org/10.34818/INDOJC.2020.5.1.372
Computer Science