Документ взят из кэша поисковой машины. Адрес оригинального документа : http://imaging.cs.msu.ru/pub/2010.DSPA.Chernomorets_Nasonov_Krylov.BlinkEEG.en.pdf Дата изменения: Sun Apr 4 23:00:00 2010 Дата индексирования: Mon Oct 1 19:54:45 2012 Кодировка:

ADAPTIVE METHOD OF EYE BLINK ARTIFACT SUPPRESSION IN EEG Chernomorets A.A., Nasonov A.V., Krylov A.S. Lomonosov Moscow State University, Faculty of Computational Mathematics and Cybernetics, Laboratory of Mathematical Methods of Image Processing http://imaging.cs.msu.ru Electroencephalography (EEG) is the neurophysiologic measurement of the electrical activity of the brain recorded by electrodes placed on the scalp. It is used to receive information about the underlying brain activity and to detect pathological signs. Unfortunately EEG is often contaminated by non-cerebral activities that can add false information to the signal or can suppress valid information. There are two major categories of artifacts: technical and physiological. The main interest lies in removing physiological artifacts, such as eye-movements, muscle or cardiac noise. In this article, suppression of blink artifacts is considered. There are two approaches in blink effect suppression. The first approach is the independent processing of EEG channels like subtracting the filtered electrooculogram (EOG) channel from EEG [1] or using wavelet transform to remove blink artifacts [2]. The second approach uses the correlation of blink functions from different channels. The most widely used methods are methods based on independent component analysis (ICA) [3]. The proposed algorithm assumes that the influence function I (t ) is the same for all channels and uses the following blink artifact model: Rk (t ) Tk (t ) k I (t ), k 1,..., N , where Rk (t ) is the recorded EEG, Tk (t ) is clean EEG signal, k is a weight coefficient. The proposed algorithm consists of the following stages: 1. Blink artifact detection using analysis of the EOG. 2. Blink artifact suppression using a method with independent processing of EEG channels. N 3. Blink influence function calculation I (t ) k 1 ( Rk (t ) Pk (t )) , where Pk (t ) is the EEG reconstructed at the previous stage. 4. Recovering the coefficients k . 5. Recovering the EEG: Tk (t ) Rk (t ) k I (t ) . Psychophysiological tests showed the reliability of the proposed algorithm. Literature P. He, G. Wilson, C. Russell "Removal of ocular artifacts from electro-encephalogram by adaptive filtering" // Medical and Biological Engineering and Computing, Vol. 42, No. 3, 2004, pp. 407­412. V. Krishnaveni, S. Jayaraman, S. Aravind, V. Hariharasudhan, K. Ramadoss "Automatic Identification and Removal of Ocular Artifacts from EEG using Wavelet Transform" // Journal of Applied Sciences Research, 5(7), 2009, pp. 741-745. N.-Y. Bian, B. Wang, Y. Cao, L. Zhang "Automatic Removal of Artifacts from EEG Data Using ICA and Exponential Analysis" // Lecture Notes in Computer Science, Vol. 3972, 2006, pp. 719­726.

1. 2. 3.