2019
DOI: 10.3390/brainsci9120352
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Abstract: The electroencephalogram signal (EEG) often suffers from various artifacts and noises that have physiological and non-physiological origins. Among these artifacts, eye blink, due to its amplitude is considered to have the most influence on EEG analysis. In this paper, a low complexity approach based on Stationary Wavelet Transform (SWT) and skewness is proposed to remove eye blink artifacts from EEG signals. The proposed method is compared against Automatic Wavelet Independent Components Analysis (AWICA) and E… Show more

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Cited by 12 publications
(3 citation statements)
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References 47 publications
(73 reference statements)
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“…The segments that contained eye-blink artifacts and transient muscular activity were selected and removed as confirmed by an expert (i.e., neuroelectrophysiologist) [73]. The presence of EOG artifacts may be indicated by a higher absolute value of skewness, as it has a significantly larger amplitude compared to an uncontaminated EEG signal [74].…”
Section: ) Eeg Data Filtering and Artifact Removalmentioning
confidence: 99%
“…The segments that contained eye-blink artifacts and transient muscular activity were selected and removed as confirmed by an expert (i.e., neuroelectrophysiologist) [73]. The presence of EOG artifacts may be indicated by a higher absolute value of skewness, as it has a significantly larger amplitude compared to an uncontaminated EEG signal [74].…”
Section: ) Eeg Data Filtering and Artifact Removalmentioning
confidence: 99%
“…In [15], the maximum overlap wavelet transform decomposition (MODWT) is used to perform timefrequency analysis on EEGs, which has been applied to filter single channel eye blinks and muscle artifacts. Based on the stationary wavelet transform (SWT), a method [16] on signal skewness has been developed to remove eye blinks. But due to the influence of individual differences, the skewness threshold for each individual fluctuates significantly, leading to poor performance.…”
Section: Introductionmentioning
confidence: 99%
“…However, this manual detection process was time-consuming and di cult as well, especially for long-time EEG recordings. Besides, manual detection was not applicable for real-time EEG applications [12]. These limitations led to develop the automatic approaches of eye-blink artifact detection.…”
Section: Introductionmentioning
confidence: 99%