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http://dx.doi.org/10.7471/ikeee.2018.22.4.1109

A Study on EEG Artifact Removal Method using Eye tracking Sensor Data  

Yun, Jong-Seob (Dept. of Computer Engineering, Seokyeong University)
Kim, Jin-Heon (Dept. of Computer Engineering, Seokyeong University)
Publication Information
Journal of IKEEE / v.22, no.4, 2018 , pp. 1109-1114 More about this Journal
Abstract
Electroencephalogram (EEG) is a tool used to study brain activity caused by external stimuli. In this process, artifacts are mixed and it is easy to distort the signal, so post-processing is necessary to remove it. Independent Component Analysis (ICA) is a widely used method for removing artifact. This method has a disadvantage in that it has excellent performance but some loss of brain wave information. In this paper, we propose a method to reduce EEG information loss by restricting the filter coverage using eye blink information obtained from Eyetracker. We then compared the results of the proposed method with the conventional method using quantization methods such as Signal to Noise Ratio (SNR) and Spectral Coherence (SC).
Keywords
EEG; Eye blink artifact; Noise canceling; ICA; Adaptive Filtering;
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1 M. Li and B.L. Lu, "Emotion classification based on gamma-band EEG," in Proc. of the 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.1223-1226, 2009. DOI:10.1109/IEMBS.2009.5334139   DOI
2 B. Abibullaev, "Learning suite of kernel feature spaces enhances SMR-based EEG-BCI classification," in Proc. of the 2017 5th International Winter Conference on Brain-Computer Interface (BCI), pp.55-59, 2017. DOI:10.1109/IWW-BCI.2017.7858158   DOI
3 M. Z. Ilyas, P. Saad, M.I. Ahmad and A. R. I. Ghani, "Classification of EEG signals for brain-computer interface applications: Performance comparison," in Proc. of the 2016 International Conference on Robotics, Automation and Sciences (ICORAS), pp.1-4, 2016. DOI:10.1109/ICORAS.2016.7872610   DOI
4 P. Tan, W.S. and L. Yu, "Applying Extreme Learning Machine to classification of EEG BCI," in Proc. of the 2016 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), pp.228-232, 2016. DOI:10.1109/CYBER.2016.7574827   DOI
5 M. Z. Ahmad, M. Saeed, S. Saleem and A. M. Kamboh, "Seizure detection using EEG: A survey of different techniques," in Proc. of the 2016 International Conference on Emerging Technologies (ICET), pp.1-6, 2016. DOI:0.1109/ICET.2016.7813209   DOI
6 Y. Yuan, G. Xun, F. Ma, Q. Suo, H. Xue, K. Jia et al., "A novel channel-aware attention framework for multi-channel EEG seizure detection via multi-view deep learning," in Proc. of the 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), pp.206-209, 2018. DOI:10.1109/BHI.2018.8333405   DOI
7 E. Kim and D. Shin, "Nonlinear and Independent Component Analysis of EEG with Artifacts," J. Fuzzy Log. Intell. Syst., Vol.12, No.5, pp.442-450, 2002.
8 N. P. Castellanos and V. A. Makarov, "Recovering EEG brain signals: Artifact suppression with wavelet enhanced independent component analysis," J. Neurosci. Methods, Vol.158, No.2, pp.300-312, 2006. DOI:10.5391/JKIIS.2002.12.5.442   DOI
9 A. Jalilifard, E. B. Pizzolato and M. K. Islam, "Emotion classification using single-channel scalp-EEG recording," in Proc. of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.845-849, 2016. DOI:10.1109/EMBC.2016.7590833   DOI
10 M. A. Klados, C. Papadelis, C. Braun, and P. D. Bamidis, "REG-ICA: A hybrid methodology combining Blind Source Separation and regression techniques for the rejection of ocular artifacts," Biomed. Signal Process. Control, Vol.6, No.3, pp.291-300, 2011. DOI:10.1016/j.bspc.2011.02.001   DOI
11 W. Zheng, "Multichannel EEG-Based Emotion Recognition via Group Sparse Canonical Correlation Analysis," IEEE Trans. Cogn. Develop. Syst, Vol.9, No.3, pp.281-290, 2017. DOI:10.1109/TCDS.2016.2587290   DOI
12 J. W. Matiko, S. Beeby, and J. Tudor, "Real time eye blink noise removal from EEG signals using morphological component analysis," in Proc. of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.13-16, 2013. DOI:10.1109/EMBC.2013.6609425   DOI