A wavelet based de-noising technique for ocular artifact correction of the electroencephalogram

Zikov, T.; Bibian, S.; Dumont, Guy A.; Huzmezan, M.; Ries, Craig R.

doi:10.1109/iembs.2002.1134407

Cited by 89 publications

(59 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this comparison test, three very bad contaminated EEG channels (from data set 2) were acquired from C3, C4 and Cz as a reference according to the International 10-20 System. Since the most denoising methods in the literature are mainly based only on WT [29], Principle Component Analysis (PCA) [30] Since the amplitude and spectrum of EEG vary from signal to signal, subject to subject, it even varies from time to time for the same subject [32]. Also, the "rhythmic" behavior of EEG is characterized by a peak in the power spectrum at specific frequencies [33], could be used to study such changes of the signal after applying our method.…”

Section: Fig 4: Refinement Of Very Bad Signals By Iterating the Methodsmentioning

confidence: 99%

“…Since the signals must therefore present a true and clear picture about brain activities, the importance of artifact removal comes to be very needful and urgent to overcome the difficulties of traditional EEG examination. Up to now, the most techniques have been proposed for removing artifacts can be classified into; Regression based method (AR) [4], Adaptive Filters [5], Principal Component Analysis (PCA) [6], Independent Component Analysis (ICA) [7], and Wavelet Transform (WT) [8]. Although these presented methods are practiced, each of them not only was designed to remove one particular artifact with its own limitations, but ruefully also considerable information of the true EEG can be lost.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Hybrid Approach for Artifacts Removal from EEG Recordings

Helal¹,

Seddik²,

Hussein³

2017

IJCA

View full text Add to dashboard Cite

The electroencephalogram (EEG) is a widely used traditional procedure for diagnosing, monitoring and managing neurological disorders. Many artifact types that often contaminate EEG remain a key challenge for precise diagnosis of brain dysfunctions and neurological disorders. Hence, artifact removal is intuitively required for accurate EEG analysis and treatment. This paper presents a new extensive method that can remove a wide variety of EEG artifacts based mainly on Template Matching approach including multiple signal-processing tools. The method was evaluated and validated on real EEG data, giving promising results that offer better capabilities to neurophysiologists in routine EEG examinations and diagnosis.

show abstract

Section: Fig 4: Refinement Of Very Bad Signals By Iterating the Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach for Artifacts Removal from EEG Recordings

Helal¹,

Seddik²,

Hussein³

2017

IJCA

View full text Add to dashboard Cite

show abstract

“…A coiflet 3 wavelet (coif3) filter has been chosen, since the shape of its mother wavelet resembles the shape of the eye blink artifact [4]. According to [3], [12], the statistical threshold T with hard thresholding function would be better, T as follows:…”

Section: Threshold and Thresholding Functionmentioning

confidence: 99%

“…But sometimes, vertical eye movement artifacts seem to produce a rise in the higher frequencies [4]. Therefore, we applied threshold denoising in bands with frequency between 0-16Hz.…”

Section: Introductionmentioning

confidence: 99%

FPGA - Implementation of Wavelet Based Denoising Technque to Remove Ocular Artifact from Single-Channel EEG Signal

Chen¹,

Li²,

Zhang³

2018

Computer Science &Amp; Information Technology

View full text Add to dashboard Cite

This paper presents the real-time implementation on FPGA of the wavelet-based denoising technique to remove the ocular artifact from the signal-channel EEG signal. The advantage of this method over conventional methods is that there is no need for the recording of the electrooculogram (EOG) signal itself. This approach papers both for eye blinks and eye movements. Discrete Wavelet Transform (DWT) is selected end the hard-thresholding is applied to the wavelet coefficients using the Statistical Threshold (ST) estimated in interested bands. This real-time architecture presents two characteristics: 1) quantization of the filter coefficients and the elimination of the multiplier to reduce the hard cost, and 2) symmetrical extension of the signal boundary to full reconstruction while the data volume is invariable. Experimental resultsshow that proposed architecture efficiently removes the ocular artifact from EEG signal.

show abstract

“…However, despite the advantages of ICA, potentially laborious manual identification of components to be removed is still needed to obtain a reliable result. Instead of exploiting time values of frequency spectra, (Zikov et al 2002) demonstrated wavelet joint timefrequency representations to be useful for EEG ocular artifact denoising. Using appropriate normalization, the so-called 'z-score' can enhance the precision of wavelet time-frequency maps, resulting in efficient artifact detection (Browne and Cutmore 2004).…”

Section: Introductionmentioning

confidence: 99%

EEG windowed statistical wavelet scoring for evaluation and discrimination of muscular artifacts

2008

View full text Add to dashboard Cite

EEG recordings are usually corrupted by spurious extra-cerebral artifacts, which should be rejected or cleaned up by the practitioner. Since manual screening of human EEGs is inherently error prone and might induce experimental bias, automatic artifact detection is an issue of importance. Automatic artifact detection is the best guarantee for objective and clean results. We present a new approach, based on the time-frequency shape of muscular artifacts, to achieve reliable and automatic scoring. The impact of muscular activity on the signal can be evaluated using this methodology by placing emphasis on the analysis of EEG activity. The method is used to discriminate evoked potentials from several types of recorded muscular artifacts-with a sensitivity of 98.8% and a specificity of 92.2%. Automatic cleaning of EEG data are then successfully realized using this method, combined with independent component analysis. The outcome of the automatic cleaning is then compared with the Slepian multitaper spectrum based technique introduced by Delorme et al ( Neuroimage 34 1443.

show abstract

A wavelet based de-noising technique for ocular artifact correction of the electroencephalogram

Cited by 89 publications

References 21 publications

A Hybrid Approach for Artifacts Removal from EEG Recordings

A Hybrid Approach for Artifacts Removal from EEG Recordings

FPGA - Implementation of Wavelet Based Denoising Technque to Remove Ocular Artifact from Single-Channel EEG Signal

EEG windowed statistical wavelet scoring for evaluation and discrimination of muscular artifacts

Contact Info

Product

Resources

About