2017
DOI: 10.3390/s17061326
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Automatic Artifact Removal in EEG of Normal and Demented Individuals Using ICA–WT during Working Memory Tasks

Abstract: Characterizing dementia is a global challenge in supporting personalized health care. The electroencephalogram (EEG) is a promising tool to support the diagnosis and evaluation of abnormalities in the human brain. The EEG sensors record the brain activity directly with excellent time resolution. In this study, EEG sensor with 19 electrodes were used to test the background activities of the brains of five vascular dementia (VaD), 15 stroke-related patients with mild cognitive impairment (MCI), and 15 healthy su… Show more

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Cited by 68 publications
(34 citation statements)
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References 103 publications
(162 reference statements)
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“…To further identify the information for each IMF, SampEn is used as a criterion to select significant IMFs. SampEn is widely used to detect artifacts because of its ability to detect the complexity of changes in brain activity (Mahajan and Morshed, 2015; Al-Qazzaz et al, 2017; Cuesta-Frau et al, 2017). SampEn is calculated as follows:…”
Section: Methodsmentioning
confidence: 99%
“…To further identify the information for each IMF, SampEn is used as a criterion to select significant IMFs. SampEn is widely used to detect artifacts because of its ability to detect the complexity of changes in brain activity (Mahajan and Morshed, 2015; Al-Qazzaz et al, 2017; Cuesta-Frau et al, 2017). SampEn is calculated as follows:…”
Section: Methodsmentioning
confidence: 99%
“…This formula is a scaled version of the universal threshold first proposed by Donoho and Johnstone ( 1994 ) that also incorporates a robust estimate of the signal variance. Soft-thresholding (Donoho, 1995 ) has been implemented in prior studies of wavelet-thresholding electrophysiological data for artifact rejection (e.g., Al-Qazzaz et al, 2017 ). As in prior W-ICA studies, given that the magnitude of artifacts can be far greater than that of neurophysiological signals, the component time series whose amplitudes are large enough to survive the wavelet-thresholding are taken as the artifact timeseries (similar to Castellanos and Makarov, 2006 ).…”
Section: The Harvard Automated Preprocessing Pipeline For Eeg (Happe)mentioning
confidence: 99%
“…Emotional changes would be elicited using different physiological signals such as galvanic skin response (GSR) [15], electrodermal activity (EDA) [17], blood volume pressure (BVP) [18], and skin temperature (ST) [19], evoked potentials (EP) [20], electrocardiogram (ECG) [21], electromyogram (EMG) [22], and electroencephalogram (EEG) [23][24][25][26][27][28][29][30]. Clinically, EEG signals have been widely used as useful indicators of different mental states such as epilepsy, Alzheimer's disease (AD) and vascular dementia (VaD) [31][32][33][34][35].…”
Section: Introductionmentioning
confidence: 99%