AN ALTERNATIVE METHOD FOR THE REMOVAL OF THE OCULAR ARTIFACT FROM THE EEG SIGNAL

Anca Mihaela LAZAR; R. URSULEAN

Anca Mihaela LAZAR ”Gr.T. Popa” University of Medicine and Pharmacy Iași
R. URSULEAN ”Gh. Asachi” Technical University Iași

Keywords: BRAIN-COMPUTER-INTERFACE, EEG ARTIFACT, MATHEMATICAL MORPHOLOGY, STRUCTURING ELEMENT

Abstract

The aim of this paper is to develop a technique of
rejection or minimization of ocular artifacts from the electroencephalographic (EEG) recordings.
Material and method: The method presented is based on mathematical morphology.
The algorithm and the individual structuring element are presented along with the particularities
concerning the parameters that characterize the structuring element. Results: The obtained
results are shown in a comprehensive form by means of an illustrating example that evidences
the efficiency of the method. Conclusions: The proposed method simplyfies the task of
removing the ocular artifacts by means of nonlinear filtering and introduces a new structural
element for the job. The results are validated by means of Fourier analysis and this clearly
shows its effectiveness.

Author Biographies

Anca Mihaela LAZAR, ”Gr.T. Popa” University of Medicine and Pharmacy Iași

School of Medical Bioengineering
Biomedical Instrumentation Department

R. URSULEAN, ”Gh. Asachi” Technical University Iași

School of Electrical Engineering
Electrotechnics and Electrical Machines Department

References

1. Fatourechi M, Bashashati A, Ward RK, Birch GE. EOG and EMG artifacts in brain interface
systems: a survey. Clin Neurophysiol 2007; 118 (3): 480-494.
2. Barlow JS. Artifact processing (rejection and minimization) in EEG data processing. In: Handbook of
Electroencephalography and Clinical Neurophysiology. vol. 2. Amsterdam: Elsevier, 1986, 1562.
3. Croft RJ, Barry RJ. Removal of ocular artifact from the EEG: a review. Clin Neurophysiol 2000;
30 (1): 5-19.
4. Lagerlund TG, Sharbrough FW, Busacker NE. Spatial filtering of multichannel electroencephalographic
recordings through principal component analysis by singular value decomposition. J
Clin Neurophysiol 1997; 14 (1): 73-82.
5. Choi S, Cichocki A, Park HM, LY. Blind source separation and independent component analysis:
a review. Neural Inf Process Lett Rev 2005; 6 (1): 157.
6. He P, Wilson G, Russell C. Removal of ocular artifacts from electro-encephalogram by adaptive
filtering. Med Biol Eng Comput 2004; 42 (3): 407-412.
7. Senthil Kumar P, Arumuganathan R, Sivakumar K, Vimal C. Removal of ocular artifacts in the EEG
through wavelet transform without using an EOG reference channel. Int J Open Probl Comput
Math 2008; 1 (3): 188-200.
8. Matheron G. Random Sets and Integral Geometry. New York: Wiley, 1974.
9. Serra J. Image Analysis and Mathematical Morphology. vol. 1. London: Academic Press, 1982.
10. Serra J. Image Analysis and Mathematical Morphology. vol. 2. Theoretical Advances. London:
Academic Press, 1988.
11. Kriðèiukaitis A, Webb DJ, Grigaliûnas A. Non-invasive evaluation of EEG quality and early life
stage development of Medaka fish (Oryzias latipes) by means of morphological image analysis
methods. Electron Electrical Eng 2009; 5 (93): 107112.
12. Maragos P, Schafer RW. Morphological filters-Part I: Their set-theoretic analysis and relations to
linear shift-invariant filters. IEEE Trans Acoust Speech Signal Process 1987; 35 (8): 1153-1169.
13. Maragos P, Schafer RW. Morphological filters-Part II: Their relations to median, order-statistic,
and stack filters. IEEE Trans Acoust Speech Signal Process 1987; 35 (8): 1170-1184.
14. Schlögl A, Anderer P, Barbanoj MJ et al. Artefact processing of the sleep EEG in the SIESTA
project. Proceeding of the EMBEC 99: 1644-1645.
15. Ghosh PK, Deguchi K. Mathematics of Shape Description - A Morphological Approach to Image
Processing and Computer Graphics. New York: Wiley, 2008.
16. Xu G, Wang J, Zhang Q, Zhang S, Zhu J. A spike detection method in EEG based on improved
morphological filter. Comput Biol Med 2007; 37: 1647-1652.