EOG artifact removal using a wavelet neural network

Nguyen, Hoang-Anh T.; Musson, John; Li, Feng; Wang, Wei; Zhang, Guangfan; Xu, Roger; Richey, Carl; Schnell, Thomas; McKenzie, Frederic D.; Jiang, Li

doi:10.1016/j.neucom.2012.04.016

Cited by 93 publications

(34 citation statements)

References 38 publications

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“…However, as a batch algorithm, ICA must be performed on all of the data with an adequate number of signals, and its computation is extremely complex and time-consuming. In contrast, the wavelet threshold algorithm can remove EOG artifacts from single-channel data with the advantage of multi-resolution analysis of wavelet transforms [24]. Nevertheless, the traditional wavelet threshold method using some soft and hard threshold functions for threshold wavelet coefficients cannot be uniformly compressed in positive and negative directions.…”

Section: Methodsmentioning

confidence: 99%

Estimation of the cool executive function using frontal electroencephalogram signals in first-episode schizophrenia patients

Yun

et al. 2016

BioMed Eng OnLine

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BackgroundIn schizophrenia, executive dysfunction is the most critical cognitive impairment, and is associated with abnormal neural activities, especially in the frontal lobes. Complexity estimation using electroencephalogram (EEG) recording based on nonlinear dynamics and task performance tests have been widely used to estimate executive dysfunction in schizophrenia.MethodsThe present study estimated the cool executive function based on fractal dimension (FD) values of EEG data recorded from first-episode schizophrenia patients and healthy controls during the performance of three cool executive function tasks, namely, the Trail Making Test-A (TMT-A), Trail Making Test-B (TMT-B), and Tower of Hanoi tasks.ResultsThe results show that the complexity of the frontal EEG signals that were measured using FD was different in first-episode schizophrenia patients during the manipulation of executive function. However, no differences between patients and controls were found in the FD values of the EEG data that was recorded during the performance of the Tower of Hanoi task.ConclusionsThese results suggest that cool executive function exhibits little impairment in first-episode schizophrenia patients.

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Section: Methodsmentioning

confidence: 99%

Estimation of the cool executive function using frontal electroencephalogram signals in first-episode schizophrenia patients

Yun

et al. 2016

BioMed Eng OnLine

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“…The main sources of artefacts are the EOG artefacts, ocular artefacts (eye movement and eye blink), noise muscles (EMG), heart signal, and various kinds of noise which is mixed with brain signals and often be the artefacts in EEG recordings [6][7][8][9][10]. Removal of artefacts is the real solution for quantitative analysis in the EEG recordings.…”

Section: Introductionmentioning

confidence: 99%

Artefacts Removal of EEG Signals with Wavelet Denoising

Turnip

Pardede

2017

MATEC Web Conf.

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Abstract. The recording of EEG signals often still contains many contaminants electrical signals that originate from non-cerebral origin such as ocular muscle activity called artefacts. The amplitude of artefacts can be quite large relative to the size of amplitude of the cortical signals of interest. In this paper, an application of wavelet denoising method for artefacts removal of EEG signals is proposed. The experiment result shows that contaminant artifact of EEG signals can significantly removed.

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“…The artifacts include eye blinking and movement, heart beating and muscle movement. Among them blink artifact has considerable influence to the analysis of EEG signals due to larger amplitude, higher occurrence rate, so that blink artifact removal is a concerned problem [1][2].…”

Section: Introductionmentioning

confidence: 99%

A Method for Blink Artifact Detection and Removal with Wavelet Transform and Hilbert Transform

Cai¹,

Chen²

2017

Proceedings of the 2016 International Conference on Biological Engineering and Pharmacy (BEP 2016)

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Abstract-This paper presents a method of blink artifact removal of single lead EEG signal. After a wavelet transform signal singularity points are detected and modulus maxima values of wavelet transform are applied to find peaks of blink artifacts. By using Hilbert transform instantaneous frequency is obtained. With the peaks as clues instantaneous frequencies are used to identify the zones appearing blink artifacts. With a stationary wavelet transform, EEG signal is decomposed to the depth on which the frequency bandwidth of approximation coefficients covers the frequency bandwidth of blink artifacts. The approximation coefficients in a blink artifact zone are estimated by the fitting data using the data outside of the blink zone. To the detail coefficients de-noising threshold value in a blink zone is estimated with the threshold values of two sides out of the blink zone. After these coefficients are corrected, the signal is reconstructed to get the EEG signal with blink artifact removal. The experimental results show that the proposed method is effective to detect and remove blink artifacts and avoid affecting the EEG signal outside the blink artifact zones.

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EOG artifact removal using a wavelet neural network

Cited by 93 publications

References 38 publications

Estimation of the cool executive function using frontal electroencephalogram signals in first-episode schizophrenia patients

Estimation of the cool executive function using frontal electroencephalogram signals in first-episode schizophrenia patients

Artefacts Removal of EEG Signals with Wavelet Denoising

A Method for Blink Artifact Detection and Removal with Wavelet Transform and Hilbert Transform

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