SWT-kurtosis based algorithm for elimination of electrical shift and linear trend from EEG signals

Shahbakhti, Mohammad; Rodrigues, Ana Santos; Augustyniak, Piotr; Broniec-Wójcik, Anna; Sološenko, Andrius; Beiramvand, Matin; Marozas, Vaidotas

doi:10.1016/j.bspc.2020.102373

Cited by 12 publications

(6 citation statements)

References 29 publications

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“…The artifact IC is identified based on physical inspection as well as computing the artifact detection parameters, energy and kurtosis. As per [20], high energy and low kurtosis [31] values are observed for artifact ICs compared to non-artifact ICs. Accordingly, from table 1 and from figure 6 (b), second channel of SSA-ICA output is identified as motion artifact component and it is applied to GMETV filter method to separate the actual artifact.…”

Section: Case Study-i: Single Artifactsmentioning

confidence: 87%

Hybrid algorithm for multi artifact removal from single channel EEG

Noorbasha

Sudha

2021

Biomed. Phys. Eng. Express

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Electroencephalogram (EEG) signals recorded from the ambulatory systems are mostly contaminated by various artifacts like, electrooculogram (EOG), motion artifacts (MA) and electrocardiogram (ECG) artifacts. These artifacts limit the accuracy in further analysis of EEG in practise. So far, several existing methods have been proposed with the combination of decomposition techniques and independent component analysis (ICA) to remove single artifacts and only few methods to remove multiple artifacts from the single channel EEG. As improperly denoised EEG signals can result in wrong diagnosis, in this work, Singular Spectrum Analysis (SSA) and ICA are jointly combined with Generalized Moreau Envelope Total Variation (GMETV) technique to simultaneously remove combinations of different artifacts from single channel EEG. In this work, the SSA is used to decompose the contaminated single channel EEG, while the ICA is employed to separate the various hidden sources as independent components (ICs). Although the ICA is adequate in source separation, there is still, some essential EEG signal data appearing as artifact in the IC. Hence, eliminating this would allow EEG signal information to be lost. The GMETV approach is proposed in this paper, to estimate the actual artifacts in order to address these issues. The estimated actual artifacts are subtracted from the artifact ICs providing the residue of wanted component of EEG. This residue is added back to the remaining ICs, to obtain the denoised EEG. Simulation results demonstrated that the proposed technique performs better compared to the existing techniques. The Relative Root Mean Square Error (RRMSE) is reduced by 12.02% and 7.22% compared to SSA-ICA and SSA-ICA-thresholding respectively. Similarly, the Correlation Coefficient (CC) is increased by 21.48% and 8.25% with respect to SSA-ICA and SSA-ICA-thresholding respectively.

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Section: Case Study-i: Single Artifactsmentioning

confidence: 87%

Hybrid algorithm for multi artifact removal from single channel EEG

Noorbasha

Sudha

2021

Biomed. Phys. Eng. Express

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“…The proposed method performance measures are contrasted with the recent existing methods, SWT kurtosis [25] and EAWICA [23]. In this SWTkurtosis based method, SWT with thresholding is used to remove the ESLT artifacts, and a kurtosis-based strategy is used to select the optimal decomposition level of SWT to reach the artifact components.…”

Section: Methods For Comparisonmentioning

confidence: 99%

“…Then, these decomposed signals are applied to ICA for artifact removal purpose. The main drawback of this method is, to identify proper artifact, it needs some predefined artifact markers [25].…”

Section: Methods For Comparisonmentioning

confidence: 99%

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Novel approach to remove Electrical Shift and Linear Trend artifact from single channel EEG

Noorbasha¹,

Sudha²

2021

Biomed. Phys. Eng. Express

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Electroencephalogram (EEG) signals are crucial to Brain-Computer Interfacing (BCI). However, these are vulnerable to a variety of unintended artifacts that could negatively impact the precise brain function assessment. This paper provides a new algorithm to eliminate Electrical Shift and Linear Trend artifact (ESLT) in EEG using Singular Spectrum Analysis (SSA) and Enhanced local Polynomial (LP) Approximation-based Total Variation (EPATV). The contaminated single channel EEG is subdivided into multiple bands of frequency components by SSA. In order to acquire all LP and TV components, EPATV filtering is applied over the contaminated component frequency band. Filtered sub-signal is collected by subtracting both the LP and TV components from the component contaminated frequency band. Then, the addition of filtered sub-signal and remaining SSA frequency band components yield the final denoised EEG signal. The effectiveness of the proposed method in this paper is evaluated using the data obtained from three databases and compared with the existing methods. From the extensive simulation results, it is inferred that the algorithm discussed in the paper is effective when compared the existing methods, exhibiting a highest averaged Correlation Coefficient (CC) of 0.9534, averaged Signal to Noise Ratio (SNR) of 10.2208dB, lowest averaged Relative Root Mean Square Error (RRMSE) value 0.2787 and averaged Mean absolute Error (MAE) in α band value of 0.0557. The algorithm presented in this paper may be a viable choice for extracting ESLT artifact from a small streaming section of the EEG without requirement of the initial calibration or enormous EEG data.

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“…Once the motion artifacts were collected, they were linearly combined with a clean ground truth EEG signal to make a semi-simulated motion artifact-contaminated EEG signal. Since the motion artifacts originate from human motions which are totally independent from other legitimate brain activities, this linear superposition assumption can be valid (Islam et al 2015;Islam et al 2020;Shahbakhti et al 2021). A publicly available EEG signal dataset, collected from 50 subjects under a wellcontrolled shielded noise-free lab condition, (Klados and Bamidis 2016) was used as a ground truth EEG signal.…”

Section: Figure 2 Overview Of the Proposed Cica-based Reference Subtr...mentioning

confidence: 99%

Paired electrodes- and contraint independent components analysis-based denoising to alleivate motion artifacts in electroencephalogram collected at construction sites

Gaang¹,

Juhyeon²,

Lee³

2022

Proceedings of the 2022 European Conference on Computing in Construction

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Despite the potential of mobile electroencephalogram (EEG) devices in managing construction workers' safety, health, and productivity, deploying mobile EEG at sites is hindered by significant motion artifacts introduced by worker movements. To address this issue, the authors propose a paired electrode-and constraint independent component analysis-based denoising that adaptively suppresses EEG motion artifacts via leveraging simultaneously collected motion artifact references. The proposed denoising was compared with an advanced benchmark on an EEG dataset collected under real human motions. Results show the proposed technique's denoising performance is statistically higher than the existing advanced benchmark. The finding of this study can improve the applicability of mobile EEG to construction sites, thereby significantly contributing to management of workers' safety, health, and productivity.

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SWT-kurtosis based algorithm for elimination of electrical shift and linear trend from EEG signals

Cited by 12 publications

References 29 publications

Hybrid algorithm for multi artifact removal from single channel EEG

Hybrid algorithm for multi artifact removal from single channel EEG

Novel approach to remove Electrical Shift and Linear Trend artifact from single channel EEG

Paired electrodes- and contraint independent components analysis-based denoising to alleivate motion artifacts in electroencephalogram collected at construction sites

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