Summary
This paper presents a new empirical method for de-noising ofocular artifacts in the electroencephalogram (EEG) records. Inmany biomedical signal processing approach, source signals arenoisy and some have kurtosis close to zero. These noise sourcesincrease the difficulty in analyzing the EEG and obtaining theclinical information. To remove this artifacts a method basedon Donoho’s de-noising method is used. Recently StationaryWavelet Transform (SWT) has been used to de-noise thecorrupted EEG signals. In this paper, statistical empiricalmethod for removing ocular artifacts from EEG recordingsthrough SWT is suggested.
Key words:EEG, de-noising, ocular artifacts, stationary wavelet transform
1. Introduction
The statistical analysis of electrical recordings of the brainactivity by an Electroencephalogram is a major problem inNeuroscience. Cerebral signals have several origins thatlead to the complexity of their identification. Therefore,the noise removal is of the prime necessity to make easierdata interpretation and representation and to recover thesignal that matches perfectly a brain functioning. Acommon problem faced during the clinical recording of theEEG signal, are the eye-blinks and movement of the eyeballs that produce ocular artifacts. It has been known forquite some time now that the Alpha rhythm of the EEG,which is the principal resting rhythm of the brain in adultswhile they are awake, is directly influenced by visualstimuli. Auditory and mental arithmetic tasks with theeyes closed lead to strong alpha waves, which aresuppressed when the eyes are opened. This property ofthe EEG has been used, ineffectively, for a long period oftime to detect eye blinks and movements. The slowresponse of thresholding, failure to detect fast eye blinksand the lack of an effective de-noising technique forcedresearchers to study the frequency characteristics of theEEG as well.Current Independent Component Analysis (ICA) methodsof artifact removal require a tedious visual classification ofthe components. P. LeVan [1] proposed a method whichautomates this process and removes simultaneouslymultiple types of artifacts. R.J. Croft [2] reviews anumber of methods of dealing with ocular artifact in theEEG, focusing on the relative merits of a variety of EOGcorrection procedures. In EEG data sets, there may besome specific components or events that may help theclinicians in diagnosis. They may tend to be transient(localized in time), prominent over certain scalp regions(localized in space) and restricted to certain ranges oftemporal and spatial frequencies (localized in scale).There has been a tremendous amount of activity andinterest in the applications of wavelet analysis to signals,in particular methods of wavelet thresholding andshrinkage [8,13] for the removal of additive noise fromcorrupted biomedical signals and images. Waveletanalysis provides flexible control over the resolution withwhich neuro-electric components and events are localizedin time, space and scale. V.J. Samar [4] describes thebasic concepts of wavelet analysis and other applications.V. Krishnaveni [9,10] discussed a method to automaticallyidentify slow varying ocular artifact zones and applyingwavelet based adaptive thresholding algorithm only to theidentified ocular artifact zones, which avoids the removalof background EEG information. The fundamentalmotivation behind these approaches is that the statistics ofmany real world signals, when wavelet transformed aresubstantially simplified.Wavelet transforms are used to analyze time varying,non-stationary signals, and EEG fall into these category ofsignals. The ability of wavelet transform is to accuratelyresolve EEG into specific time and frequency componentslead to several analysis applications and one among themis de-noising. The wavelet transform of the noisy signalgenerates the wavelet coefficients which denote thecorrelation coefficients between the noisy EEG and thewavelet function. Depending on the choice of mother wavelet function, larger coefficients will be generatedcorresponding to the noise affected zones. The largercoefficients will be an estimate of noiseTatjana Zikov [12] proposed a wavelet based de-noisingof the EEG signal to correct for the presence of the ocularartifact. In this paper, we proposed a simple statisticalempirical de-noising formula for removing artifacts in theEEG signals without using any reference signals. Thisformula very much reduces the complexity and time factor.



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