Removal of Electrooculogram Artifacts from Electroencephalogram Using Canonical Correlation Analysis with Ensemble Empirical Mode Decomposition

Yang, Banghua; Zhang, Tao; Zhang, Yunyuan; Liu, Wanquan; Wang, Jianguo; Duan, Kailiang

doi:10.1007/s12559-017-9478-0

Cited by 32 publications

(12 citation statements)

References 24 publications

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“…In the whole data set and all point, every IMF must be satisfies that the number of extrema are same with the number of zero crossings or differ at most by one, and the mean value of the envelope defined by the maxima and minima must be zero. Therefore, the EMD technique is empirical and data driven technique, whereas other methods depend on the selections of basic functions, such as wavelet analysis [73]. A sifting procedure is taken to calculate the IMF of a given signals and the steps shown below:Set b[n] equal to input signal sequence x[n].Calculate all the local maxima and local minima, and connect them separately with cubic spline interpolation.…”

Section: Single Artifacts Removal Techniquesmentioning

confidence: 99%

Removal of Artifacts from EEG Signals: A Review

Jiang

Bian

Tian

2019

Sensors

520

329

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Electroencephalogram (EEG) plays an important role in identifying brain activity and behavior. However, the recorded electrical activity always be contaminated with artifacts and then affect the analysis of EEG signal. Hence, it is essential to develop methods to effectively detect and extract the clean EEG data during encephalogram recordings. Several methods have been proposed to remove artifacts, but the research on artifact removal continues to be an open problem. This paper tends to review the current artifact removal of various contaminations. We first discuss the characteristics of EEG data and the types of different artifacts. Then, a general overview of the state-of-the-art methods and their detail analysis are presented. Lastly, a comparative analysis is provided for choosing a suitable methods according to particular application.

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Section: Single Artifacts Removal Techniquesmentioning

confidence: 99%

Removal of Artifacts from EEG Signals: A Review

Jiang

Bian

Tian

2019

Sensors

520

329

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“…According to [7], the EOG is a major EEG artifact which can corrupt the original signal during recording time. The efficient method for removing the EOG artifacts from the original EEG will help obtain useful feature extraction and enhance classification rate accuracy.…”

Section: Comparing the Proposed Methods With State-of-the-art Methodsmentioning

confidence: 99%

“…Yang et al in [7] proposed an artificial method for removing the EOG artifacts from the EEG raw. The proposed method (CCA-EEMD) involves three steps.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, EEG signals have been used in medical applications for prevention, detection diagnosis, rehabilitation and restoration. This technique has also been used for non-medical applications, such as education and self-regulation, neuromarketing and advertisement, neuroergonomics and smart environment, games and entertainment, and learning and education [6], [7]. Recently, EEG signals have been used as a new biometric technique in security and authentication applications [1], [6].…”

Section: Introductionmentioning

confidence: 99%

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EEG Signals Denoising Using Optimal Wavelet Transform Hybridized With Efficient Metaheuristic Methods

et al. 2020

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Background. The most common and successful technique for signal denoising with nonstationary signals, such as electroencephalogram (EEG) and electrocardiogram (ECG) is the wavelet transform (WT). The success of WT depends on the optimal configuration of its control parameters which are often experimentally set. Fortunately, the optimality of the combination of these parameters can be measured in advance by using the mean squared error (MSE) function. Method. In this paper, five powerful metaheuristic algorithms are proposed to find the optimal WT parameters for EEG signal denoising which are harmony search (HS), β-hill climbing (β-hc), particle swarm optimization (PSO), genetic algorithm (GA), and flower pollination algorithm (FPA). It is worth mentioning that this is the initial investigation of using optimization methods for WT parameter configuration. This paper then examines which efficient algorithm has obtained the minimum MSE and the best WT parameter configurations. Result. The performance of the proposed algorithms is tested using two standard EEG datasets, namely, Kiern's EEG dataset and EEG Motor Movement/Imagery dataset. The results of the proposed algorithms are evaluated using five common criteria: signal-to-noise-ratio (SNR), SNR improvement, mean square error (MSE), root mean square error (RMSE), and percentage root mean square difference (PRD). Interestingly, for almost all evaluating criteria, FPA achieves the best parameters configuration for WT and empowers this technique to efficiently denoise the EEG signals for almost all used datasets. To further validate the FPA results, a comparative study between the FPA results and the results of two previous studies is conducted, and the findings favor to FPA. Conclusion. In conclusion, the results show that the proposed methods for EEG signal denoising can produce better results than manual configurations based on ad hoc strategy. Therefore, using metaheuristic approaches to optimize the parameters for EEG signals positively affects the denoising process performance of the WT method.

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“…This limitation is due to the volume conduction effect (Niedermeyer and Silva 2005); the bone structure filters out the higher frequency part, mixing the brain sources signals and reducing the signal-to-noise ratio (SNR). Consequently, spatial filtering methods are developed to enhance SNR, such as common spatial patterns (CSP) (Johannes et al 1999), independent component analysis (ICA) (Wang and James 2006), xDAWN (Rivet et al 2009) or canonical correlation analysis (CCA) (Yang et al 2017). Filtered signals define a feature space where machine learning methods are used to classify trials.…”

Section: Review Of Riemannian Distances and Divergences Applied To Smentioning

confidence: 99%

Review of Riemannian Distances and Divergences, Applied to SSVEP-based BCI

Chevallier

Kalunga²,

Barthélemy³

2020

Neuroinform

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Background The first generation of brain-computer interfaces (BCI) classifies multi-channel electroencephalographic (EEG) signals, enhanced by optimized spatial filters. The second generation directly classifies covariance matrices estimated on EEG signals, based on straightforward algorithms such as the minimum-distance-to-Riemannian-mean (MDRM). Classification results vary greatly depending on the chosen Riemannian distance or divergence, whose definitions and reference implementations are spread across a wide mathematical literature.Methods This paper reviews all the Riemannian distances and divergences to process covariance matrices, with an implementation compatible with BCI constraints. The impact of using different metrics is assessed on a steady-state visually evoked potentials (SSVEP) dataset, evaluating centers of classes and classification accuracy.Results and Conclusions Riemannian approaches embed crucial properties to process EEG data. The Riemannian centers of classes outperform Euclidean ones both in offline and online setups. Some Riemannian

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Removal of Electrooculogram Artifacts from Electroencephalogram Using Canonical Correlation Analysis with Ensemble Empirical Mode Decomposition

Cited by 32 publications

References 24 publications

Removal of Artifacts from EEG Signals: A Review

Removal of Artifacts from EEG Signals: A Review

EEG Signals Denoising Using Optimal Wavelet Transform Hybridized With Efficient Metaheuristic Methods

Review of Riemannian Distances and Divergences, Applied to SSVEP-based BCI

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