Automatic and tunable algorithm for EEG artifact removal using wavelet decomposition with applications in predictive modeling during auditory tasks

Bajaj, Nikesh; Carrión, Jesús Requena; Bellotti, Francesco; Berta, Riccardo; Gloria, A. De

doi:10.1016/j.bspc.2019.101624

Cited by 45 publications

(30 citation statements)

References 33 publications

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“…ATAR algorithm: A recent study has shown that approaches based on above thresholds are very aggressive, since, statistically, a few wavelet coefficients of any signal will always fall above these thresholds [14]. In contrast, an Automatic and Tunable Artefact Removal (ATAR) Algorithm based on WPD was proposed [41], which provides three different wavelet filtering modes and a tunable parameter. As shown a block diagram of ATAR algorithm in Figure 8, a single channel EEG signal xn ðÞis first split into smaller windows x w n ðÞ , apply L-level WPD to get wavelet coefficients w ¼ X L k ðÞ¼WPD x w n ðÞ ðÞ , then wavelet coefficients are filtered using wavelet filteringŵ ¼ λ w ðÞ to reconstruct signalxn ðÞ from corrected windowsx w n ðÞ .…”

Section: Artefact Removal Algorithms Using Dwt and Wpdmentioning

confidence: 99%

“…Among wavelet-based approaches, using ATAR gives much control over Global and STD based threshold selection. Other quantitative analyses of the above-mentioned approaches are discussed in the article [41], which also demonstrate the effect of tuning parameter and filtering modes on different predictive tasks of EEG signal. The formulation of relationship, algorithmic implementation details, and comparative results are given in article [41].…”

Section: Artefact Removal Algorithms Using Dwt and Wpdmentioning

confidence: 99%

“…Wavelet filtering modes for ATAR algorithm. For θ α ¼ 200, θ γ ¼ 0:8θ α ¼ 160, θ β ¼ 2θ α ¼ 400[41].…”

mentioning

confidence: 99%

“…Comparison of artefact removal approaches from[41].algorithm, Medical and Biological Engineering and Computing 43 (2005) 290-295.…”

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confidence: 99%

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Wavelets for EEG Analysis

Bajaj¹

2021

Wavelet Theory

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This chapter introduces the applications of wavelet for Electroencephalogram (EEG) signal analysis. First, the overview of EEG signal is discussed to the recording of raw EEG and widely used frequency bands in EEG studies. The chapter then progresses to discuss the common artefacts that contaminate EEG signal while recording. With a short overview of wavelet analysis techniques, namely; Continues Wavelet Transform (CWT), Discrete Wavelet Transform (DWT), and Wavelet Packet Decomposition (WPD), the chapter demonstrates the richness of CWT over conventional time-frequency analysis technique e.g. Short-Time Fourier Transform. Lastly, artefact removal algorithms based on Independent Component Analysis (ICA) and wavelet are discussed and a comparative analysis is demonstrated. The techniques covered in this chapter show that wavelet analysis is well-suited for EEG signals for describing time-localised event. Due to similar nature, wavelet analysis is also suitable for other biomedical signals such as Electrocardiogram and Electromyogram.

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Section: Artefact Removal Algorithms Using Dwt and Wpdmentioning

confidence: 99%

Section: Artefact Removal Algorithms Using Dwt and Wpdmentioning

confidence: 99%

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Wavelets for EEG Analysis

Bajaj¹

2021

Wavelet Theory

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“…We considered the most popular methods of signal preprocessing -wavelet transform and decomposition into a fast Fourier series. In many researches, the fast Fourier transform is used in conjunction with the wavelet transform [25,26,27,28,29]. The wavelet transform carries a huge amount of information about the signal, but, on the other hand, has a strong redundancy, since each point of the phase plane affects its result.…”

Section: Wavelet Transformsmentioning

confidence: 99%

Deep learning and machine learning for EEG signal processing on the example of recognizing the disease of alcoholism

Rakhmatulin

2021

Preprint

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Alcoholism is one of the most common diseases in the world. This type of substance abuse leads to mental and physical dependence on ethanol-containing drinks. Alcoholism is accompanied by progressive degradation of the personality and damage to the internal organs. Today still not exists a quick diagnosis method to detect this disease. This article presents the method for the quick and anonymous alcoholism diagnosis by neural networks. For this method, don't need any private information about the subject. For the implementation, we considered various algorithms of machine learning and deep neural networks. In detail analyzed the correlation of the signals from electrodes by neural networks. The wavelet transforms and the fast Fourier transform was considered. The manuscript demonstrates that the deep neural network which operates only with a dataset of EEG correlation signals can anonymously classify the alcoholic and control groups with high accuracy. On the one hand, this method will allow subjects to be tested for alcoholism without any personal data, which will not cause inconvenience or shame in the subject, and on the other hand, the subject will not be able to deceive specialists who diagnose the subject for the presence of the disease.

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Hybrid time‐reassigned multisynchrosqueezing transform‐Picard‐based automated electroencephalography artifact correction methodology for brain–computer interface applications

Dovedi

Upadhyay

Kumar

2021

Int J Imaging Syst Tech

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The electroencephalographic activity is often contaminated with non-cerebral origins resulting in erroneous classification in brain-computer interface (BCI) applications. In the present work, a novel automated Electroencephalogram artifact correction methodology is proposed. Preconditional independent component analysis (ICA) is utilized for de-mixing the electroencephalographic activity into original components. The sparse entropy-based thresholding criterion is developed to automatically identify the artifactual components and the time-reassigned multisynchrosqueezing transform coefficients are estimated.The transform coefficients are processed to retain leaked cerebral activity into artifactual components. Finally, inverse transformation and inverse ICA are performed to attain artifact-free electroencephalographic activity. The efficiency of the proposed methodology is validated on simulated and real electroencephalographic activity. The results illustrate the efficacy of the proposed methodology on both simulated and real electroencephalography activities in retaining useful cerebral information and rejecting artifactual origins, with a potential to be used in various BCI applications.

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Automatic and tunable algorithm for EEG artifact removal using wavelet decomposition with applications in predictive modeling during auditory tasks

Cited by 45 publications

References 33 publications

Wavelets for EEG Analysis

Wavelets for EEG Analysis

Deep learning and machine learning for EEG signal processing on the example of recognizing the disease of alcoholism

Hybrid time‐reassigned multisynchrosqueezing transform‐Picard‐based automated electroencephalography artifact correction methodology for brain–computer interface applications

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