Proceedings ArticleDOI
A method for automatic removal of eye blink artifacts from EEG based on EMD-ICA
Mumtaz Hussain Soomro,Nasreen Badruddin,Mohd Zuki Yusoff,Aamir Saeed Malik +3 more
- pp 129-134
TLDR
A new hybrid algorithm that automatically removes the eye blink artifact from the EEG, based on Empirical Mode Decomposition (EMD) and Independent Component Analysis (ICA) is proposed and demonstrates that proposed method recovers the EEG data by removing the eye blinking artifacts reliably.Abstract:
The electroencephalography (EEG) recordings are mostly contaminated by eye blink artifacts. It is very difficult to analyze and interpret the EEG signal due to frequent occurrence of the eye blink artifact. In this paper, a new hybrid algorithm that automatically removes the eye blink artifact from the EEG, based on Empirical Mode Decomposition (EMD) and Independent Component Analysis (ICA) is proposed. The proposed algorithm is evaluated on simulated EEG to calculate correlation coefficient and signal-to-artifact ratio (SAR). A non-corrected EEG was simulated to have a SAR of -19.1673 dB. From the simulation results, the highest average correlation coefficient and SAR of corrected EEG from non-corrected EEG are obtained as 0.871094 and 2.71645 dB respectively by applying proposed algorithm. The results demonstrate that proposed method recovers the EEG data by removing the eye blink artifacts reliably. In addition, the proposed method is applied on real spontaneous EEG data with eye blink artifact.read more
Citations
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Journal ArticleDOI
Automatic correction of ocular artifacts in the EEG: a comparison of regression-based and component-based methods*1
TL;DR: A modified regression approach using Bayesian adaptive regression splines to filter the electrooculogram (EOG) before computing correction factors supported the use of regression-based and PCA-based ocular artifact correction and suggested a need for further studies examining possible spectral distortion from ICA-based corrections.
Journal ArticleDOI
Comparative Study of Wavelet-Based Unsupervised Ocular Artifact Removal Techniques for Single-Channel EEG Data
TL;DR: It is demonstrated that the WT can be an effective tool for unsupervised OA removal from single-channel EEG data for real-time applications.
Proceedings ArticleDOI
Automatic eye-blink artifact removal method based on EMD-CCA
TL;DR: Computational assessment of corrected EEG waveforms reveals that the proposed algorithm retrieves the EEG data by removing the eye blink artifacts reliably and compared to other eye blink artifact removal techniques, the proposed method has two benefits.
Journal ArticleDOI
Removal of Eye Blink Artifacts From EEG Signals Using Sparsity
TL;DR: Two sparsity-based techniques namely morphological component analysis (MCA) and K-SVD-based artifact removal method have been evaluated and it is shown that without using any computationally expensive algorithms, only with the use of over-complete dictionaries the proposed sparsity to eliminate EB artifacts accurately from the EEG signals.
Classification of Artefacts in EEG Signal Recordings and Overview of Removing Techniques
Avinash Tandle,Nandini Jog +1 more
TL;DR: This paper classifies the artefacts from the database collected at Dr. R. N. Cooper Mun.
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