Effects of Artifacts Rejection on EEG Complexity in Alzheimer’s Disease

Labate, Domenico; Foresta, Fabio La; Mammone, Nadia; Morabito, Francesco Carlo

doi:10.1007/978-3-319-18164-6_13

Cited by 9 publications

(7 citation statements)

References 18 publications

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“…Such physiological artifacts may interfere with neural information and even be used as normal phenomena to misleadingly drive a practical application such as brain-computer interface [9]. Furthermore, artifacts might also imitate cognitive or pathologic activity and therefore bias the visual interpretation and diagnosis in clinical research such as sleep order, Alzheimer’s disease [10,11], etc. Therefore, the requirement of artifacts identification and removal, either in clinical diagnosis or practical applications, are the most important preprocessing step prior to be utilized.…”

Section: Introductionmentioning

confidence: 99%

Removal of Artifacts from EEG Signals: A Review

Jiang

Bian

Tian

2019

Sensors

523

368

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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: Introductionmentioning

confidence: 99%

Removal of Artifacts from EEG Signals: A Review

Jiang

Bian

Tian

2019

Sensors

523

368

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“…The entropy and complexity can be calculated with a short time series and fast speed, without affecting the accuracy [16,17]. Many studies have used approximate entropy (ApEn), sample entropy (SampEn), and Lempel-Ziv complexity (LZC) to study the EEG signals of traumatic brain injury, cerebral ischemia, Alzheimer's disease, depression, epilepsy, and other diseases [18][19][20][21]. Therefore, it is advantageous to use entropy and complexity to analyze EEG signals.…”

Section: Of 14mentioning

confidence: 99%

“…The overall calculation was too large, so four time-windows of 10 s were truncated from the acquired EEG data for the analysis. Different frequency bands were decomposed into delta1 (0.5-2 Hz), delta2 (2-4 Hz), theta (4-8Hz), alpha (8-13 Hz), beta1 (13)(14)(15)(16)(17)(18)(19)(20), and beta2 (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) bands. The collected signals had a total of eight channels, as follows: four channels for the brain-injured area and four channels for the non-injured area.…”

Section: Eeg Signal Processingmentioning

confidence: 99%

Increased Sample Entropy in EEGs During the Functional Rehabilitation of an Injured Brain

Cheng

Wen-wei

Liu

et al. 2019

Entropy

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Complex nerve remodeling occurs in the injured brain area during functional rehabilitation after a brain injury; however, its mechanism has not been thoroughly elucidated. Neural remodeling can lead to changes in the electrophysiological activity, which can be detected in an electroencephalogram (EEG). In this paper, we used EEG band energy, approximate entropy (ApEn), sample entropy (SampEn), and Lempel–Ziv complexity (LZC) features to characterize the intrinsic rehabilitation dynamics of the injured brain area, thus providing a means of detecting and exploring the mechanism of neurological remodeling during the recovery process after brain injury. The rats in the injury group (n = 12) and sham group (n = 12) were used to record the bilateral symmetrical EEG on days 1, 4, and 7 after a unilateral brain injury in awake model rats. The open field test (OFT) experiments were performed in the following three groups: an injury group, a sham group, and a control group (n = 10). An analysis of the EEG data using the energy, ApEn, SampEn, and LZC features demonstrated that the increase in SampEn was associated with the functional recovery. After the brain injury, the energy values of the delta1 bands on day 4; the delta2 bands on days 4 and 7; the theta, alpha, and beta bands and the values of ApEn, SampEn, and LZC of the cortical EEG signal on days 1, 4 and 7 were significantly lower in the injured brain area than in the non-injured area. During the process of recovery for the injured brain area, the values of the beta bands, ApEn, and SampEn of the injury group increased significantly, and gradually became equal to the value of the sham group. The improvement in the motor function of the model rats significantly correlated with the increase in SampEn. This study provides a method based on EEG nonlinear features for measuring neural remodeling in injured brain areas during brain function recovery. The results may aid in the study of neural remodeling mechanisms.

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“…Power frequency artifacts have distinguishable frequency (50/60 Hz) with much higher amplitude than that of the real EEG signal in some situations (Jiang et al 2019). Obviously, these kinds of artifact have impacts on EEG signals, such as reducing the signal-to-noise ratio (SNR), interfering with the analysis and confusing the results (Labate et al 2015, Mannan et al 2018. EEG signals contaminated by artifacts cannot be directly used for subsequent analysis (Keil et al 2014, Uriguen andGarcia-Zapirain 2015), and the desired high-quality EEG signals can be obtained by high-quality EEG recordings or using kinds of artifact detection and removal methods in EEG practice.…”

Section: Introductionmentioning

confidence: 99%

Quantitative signal quality assessment for large-scale continuous scalp electroencephalography from a big data perspective

et al. 2023

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Despite electroencephalogram (EEG) being a widely used neuroimaging technique with an excellent temporal resolution, in practice, the signals are heavily contaminated by artifacts masking responses of interest in an experiment. It is thus essential to guarantee a prompt and effective detection of artifacts that provides quantitative quality assessment (QA) on raw EEG data. This type of pipeline is crucial for large-scale EEG studies. However, current EEG QA studies are still limited. In this study, combined from a big data perspective, we therefore describe a quantitative signal quality assessment pipeline, a stable and general threshold-based QA pipeline that automatically integrates artifact detection and new QA measures to assess continuous resting-state raw EEG data. One simulation dataset and two resting-state EEG datasets from 42 healthy subjects and 983 clinical patients were utilized to calibrate the QA pipeline. The results demonstrated that 1) the QA indices selected are sensitive: they almost strictly and linearly decreased as the noise level increased; 2) stable, replicable QA thresholds are valid for other experimental and clinical EEG datasets; and 3) use of the QA pipeline on these datasets revealed that high-frequency noises are the most common noises in EEG practice. The QA pipeline is also deployed in the WeBrain cloud platform (https://webrain.uestc.edu.cn/, the Chinese EEG Brain Consortium portal). These findings suggested that the proposed QA pipeline may be a stable and promising approach for quantitative EEG signal quality assessment in large-scale EEG studies.

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Effects of Artifacts Rejection on EEG Complexity in Alzheimer’s Disease

Cited by 9 publications

References 18 publications

Removal of Artifacts from EEG Signals: A Review

Removal of Artifacts from EEG Signals: A Review

Increased Sample Entropy in EEGs During the Functional Rehabilitation of an Injured Brain

Quantitative signal quality assessment for large-scale continuous scalp electroencephalography from a big data perspective

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