Eye blink is an important and inevitable artifact during scalp electroencephalogram (EEG) recording. The main problem in EEG signal processing is how to identify eye blink components automatically with independent component analysis (ICA). Taking into account the fact that the eye blink as an external source has a higher sum of correlation with frontal EEG channels than all other sources due to both its location and significant amplitude, in this paper, we proposed a method based on correlation index and the feature of power distribution to automatically detect eye blink components. Furthermore, we prove mathematically that the correlation between independent components and scalp EEG channels can be translating directly from the mixing matrix of ICA. This helps to simplify calculations and understand the implications of the correlation. The proposed method doesn’t need to select a template or thresholds in advance, and it works without simultaneously recording an electrooculography (EOG) reference. The experimental results demonstrate that the proposed method can automatically recognize eye blink components with a high accuracy on entire datasets from 15 subjects.
References
[1]
Krishnaveni, V.; Jayaraman, S.; Aravind, S.; Hariharasudhan, V.; Ramadoss, K. Automatic identification and Removal of ocular artifacts from EEG using Wavelet transform. Measur. Sci. Rev. 2006, 6, 45–57.
[2]
Fatourechi, M.; Bashashati, A.; Ward, R.K.; Birch, G.E. EMG and EOG artifacts in brain computer interface systems: A survey. Clin. Neurophysiol. 2007, 118, 480–494.
[3]
Kelly, J.W.; Siewiorek, D.P.; Smailagic, A.; Collinger, J.L.; Weber, D.J.; Wang, W. Fully automated reduction of ocular artifacts in high-dimensional neural data. IEEE Trans. Biomed. Eng. 2011, 58, 598–606.
[4]
Croft, R.J.; Barry, R.J. Removal of ocular artifact from the EEG: A review. Clin. Neurophysiol. 2000, 30, 5–19.
[5]
Postelnicu, C.-C.; Talaba, D. P300-based brain-neuronal computer interaction for spelling applications. IEEE Trans. Biomed. Eng. 2013, 60, 534–543.
[6]
Gratton, G. Dealing with artifacts: The EOG contamination of the event-related brain potential. Behav. Res. Methods Instrum. Comput. 1998, 30, 44–53.
[7]
Barbato, G.; Ficca, G.; Muscettola, G.; Fichele, M.; Beatrice, M.; Rinaldi, F. Diurnal variation in spontaneous eye-blink rate. Psychiatry Res. 2000, 93, 145–151.
[8]
Caffier, P.P.; Erdmann, U.; Ullsperger, P. Experimental evaluation of eye-blink parameters as a drowsiness measure. Eur. J. Appl. Physiol. 2003, 89, 319–325.
[9]
Borghini, G.; Vecchiato, G.; Toppi, J.; Astolfi, L.; Maglione, A.; Isabella, R.; Caltagirone, C.; Kong, W.; Wei, D.; Zhou, Z.; et al. Assessment of mental fatigue during car driving by using high resolution EEG activity and neurophysiologic indices. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), California, CA, USA, 28 August–1 September 2012; pp. 6442–6445.
[10]
Stern, J.A.; Boyer, D.; Schroeder, D. Blink rate: A possible measure of fatigue. Hum. Factors 1994, 36, 285–297.
Nicolas-Alonso, L.F.; Gomez-Gil, J. Brain computer interfaces, a review. Sensors 2012, 12, 1211–1279.
[16]
Schl？gl, A.; Keinrath, C.; Zimmermann, D.; Scherer, R.; Leeb, R.; Pfurtscheller, G. A fully automated correction method of EOG artifacts in EEG recordings. Clin. Neurophys. 2007, 118, 98–104.
[17]
Joyce, C.A.; Gorodnitsky, I.F.; Kutas, M. Automatic removal of eye movement and blink artifacts from EEG data using blind component separation. Psychophysiology 2004, 41, 313–325.
[18]
Bian, N.Y.; Wang, B.; Cao, Y. Automatic removal of artifacts from EEG data using ICA and nonlinear exponential analysis. Acta Biophys. Sin. 2006, 22, 149–156.
[19]
Hu, S.; Stead, M.; Worrell, G.A. Automatic identification and removal of scalp reference signal for intracranial EEGs based on independent component analysis. IEEE Trans. Biomed. Eng. 2007, 54, 1560–1572.
[20]
Herrero, G.G.; Clercq, W.D.; Anwar, H. Automatic Removal of Ocular Artifacts in the EEG without an EOG Reference Channel. Proceedings of the 7th Nordic Signal Processing Symposium (NORSIG), Reykjavik, Iceland, 7–9 June 2006; pp. 130–133.
[21]
Mammone, N.; Morabito, F.C. Enhanced automatic artifact detection based on independent component analysis and Renyi's entropy. Neural Netw. 2008, 21, 1029–1040.
[22]
Delorme, A.; Makeig, S.; Sejnowski, T. Automatic Artifact Rejection for EEG Data Using High-Order Statistics and Independent Component Analysis. Proceedings of the 3rd International Workshop on Independent Component Analysis and Blind Signal Separation, San Diego, CA, USA, 9–12 December 2001; pp. 457–462.
[23]
Zhu, D.; Tong, J.; Chen, Y. An ICA-Based Method for Automatic Eye Blink Artifact Correction in Multi-Channel EEG. Proceedings of the 5th Conference on Information Technology and Application in Biomedicine, in Conjunction with the 2nd International Symposium & Summer School on Biomedical and Health Engineering, Shenzhen, China, 30–31 May 2008; pp. 338–341.
[24]
Delsanto, S.; Lamberti, F.; Montrucchio, B. Automatic Ocular Artifact Rejection Based on Independent Component Analysis and Eyeblink Detection. Proceedings of the First International IEEE Engineering in Medicine and Biology Society (EMBS) Conference on Neural Engineering, Capri Island, Italy, 20–22 March 2003; pp. 309–312.
[25]
Li, Y.; Ma, Z.; Lu, W.; Li, Y. Automatic removal of the eye blink artifact from EEG using an ICA-based template matching approach. Physiol. Measur. 2006, 27, 425–436.
[26]
Phothisonothai, M.; Tsubomi, H.; Kondo, A.; Kikuchi, M.; Yoshimura, Y.; Minabe, Y.; Watanabe, K. Linear and Nonlinear Features for Automatic Artifacts Removal from MEG Data Based on ICA. Proceedings of the 2012 Asia-Pacific Signal & Information Processing Association Annual Summit and Conference (APSIPA ASC), California, USA, 3–6 December 2012; pp. 1–9.
[27]
Mognon, A.; Jovicich, J.; Bruzzone, L.; Buiatti, M. ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features. Psychophysiology 2010, 48, 229–240.
[28]
Hyv？rinen, A.; Oja, E. Independent component analysis: Algorithms and applications. Neural Netw. 2000, 13, 411–430.
[29]
Comon, P. Independent component analysis: A new concept? Signal Process. 1994, 36, 287–314.
Lee, T.-W.; Girolami, M.; Sejnowski, T.J. Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Sub-Gaussian and Super-Gaussian Sources. Proceedings of the 4th Joint Symposium of Neural Computation, La Jolla, CA, USA, 17 May 1999; Volume 7, pp. 132–139.
[32]
Derrick, T.R.; Bates, B.T.; Dufek, J.S. Evaluation of time-series data sets using the Pearson product-moment correlation coefficient. Med. Sci. Sports Exerc. 1994, 26, 919–928.
[33]
Kong, W.; Zhao, X.; Hu, S.; Zhang, J.; Dai, G.; Vecchiato, G.; Babiloni, F. The Study of Memorization Index Based on W-GFP during the Observation of TV Commercials. Proceedings of the International Conference on Systems and Informatics (ICSAI), Yantai, China, 19–20 May 2012; pp. 2198–2202.
[34]
Delorme, A.; Makeig, S. EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 2004, 134, 9–21.
[35]
Mantini, D.; Franciotti, R.; Romani, G.L.; Pizzella, V. Improving MEG source localizations: An automated method for complete artifact removal based on independent component analysis. Neurolmage 2008, 40, 160–173.
[36]
Barbati, G. Optimization of an independent component analysis approach for artifact identification and removal in magnetoencephalographic signals. Clin. Neurophysiol. 2004, 115, 1220–1232.
