MINIMIZATION OF EOG ARTIFACTS FROM CORRUPTED EEG SIGNALS USING A NEURAL-NETWORK APPROACH

被引:5
作者
SADASIVAN, PK [1 ]
DUTT, DN [1 ]
机构
[1] INDIAN INST SCI,DEPT ELECT COMMUN ENGN,BANGALORE 560012,KARNATAKA,INDIA
关键词
ELECTROENCEPHALOGRAM; EYE MOVEMENT ARTIFACTS; VOLTERRA NONLINEARITY; NOISE MINIMIZATION; NONLINEAR OPTIMIZATION; NEURAL NETWORKS;
D O I
10.1016/0010-4825(94)90042-6
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
In this paper, we propose a neural network (NN) approach to the enhancement of EEG signals in the presence of EOG artefacts. We recast the EEG enhancement problem into the optimization framework by developing an appropriate cost function. The cost function is nothing but the energy in the enhanced EEG signal obtained through a nonlinear filter formulation, unlike the conventionally-used linear filter formulation. The minimization property of feedback-type neural networks is exploited to solve this problem. An analysis has been performed to characterize the stationary points of the suggested energy function. The hardware set-up of the developed neural network has also been derived. The optimum nonlinear filter coefficients obtained from this minimization algorithm are used to estimate the EOG artefact which is then subtracted from the corrupted EEG signal, sample by sample, to get the artefact minimized signal. The time plots as the LP spectrum show that the proposed method is very effective. Thus the power and efficacy of the NN approach have been exploited for the purpose of minimizing EOG artefacts from corrupted EEG signals.
引用
收藏
页码:441 / 449
页数:9
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