Computerized processing of EEG–EOG–EMG artifacts for multi-centric studies in EEG oscillations and event-related potentials

Moretti, Davide Vito

doi:10.1016/s0167-8760(02)00153-8

Cited by 247 publications

(170 citation statements)

References 55 publications

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“…The EEG epochs with ocular, muscular and other types of artifact were preliminary identified by a computerized automatic procedure (Moretti et al, 2003). Two expert electroencephalographists manually double-checked and confirmed the automatic selections.…”

Section: Eeg Recordingsmentioning

confidence: 99%

“…Two expert electroencephalographists manually double-checked and confirmed the automatic selections. The EEG epochs contaminated by ocular artifacts were corrected by a standard autoregressive method (Moretti et al, 2003). The EEG epochs in which the ocular artifacts could not be removed with that procedure were rejected from the analysis.…”

Section: Eeg Recordingsmentioning

confidence: 99%

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Individual analysis of EEG frequency and band power in mild Alzheimer's disease

Moretti

Babiloni

Binetti

et al. 2004

Clinical Neurophysiology

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327

251

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Objective: This EEG study investigates the role of the cholinergic system, cortico-cortical connections, and sub-cortical white matter on the relationship between individual EEG frequencies and their relative power bands.Methods: EEGs were recorded at rest in 30 normal elderly subjects (Nold), 60 mild Alzheimer disease (AD) and 20 vascular dementia (VaD) patients, comparable for Mini Mental State Evaluation scores . Individual EEG frequencies were indexed by the theta/alpha transition frequency (TF) and by the individual alpha frequency (IAF) with power peak in the extended alpha range (5 -14 Hz). Relative power was separately computed for delta, theta, alpha1, alpha2, and alpha3 bands, on the basis of the TF and IAF.Results: Using normal subjects as a reference, VaD patients showed 'slowing' of alpha frequency (TF-IAF) and lower alpha2 power; Mild AD patients showed lower alpha2 and alpha3 power; delta power was higher in both AD and VaD patients; Theta power was higher only in VaD patients.Conclusions: Individual analysis of the alpha frequency and power can discriminate mild AD from VaD and normal elderly subjects.Significance: This analysis may probe pathophysiological mechanisms causing AD and VaD.

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Section: Eeg Recordingsmentioning

confidence: 99%

Section: Eeg Recordingsmentioning

confidence: 99%

Individual analysis of EEG frequency and band power in mild Alzheimer's disease

Moretti

Babiloni

Binetti

et al. 2004

Clinical Neurophysiology

Self Cite

327

251

View full text Add to dashboard Cite

show abstract

“…The computerized automatic procedure was based by a software package that included automatic procedures for (i) EOG artifact detection and correction, (ii) EMG analysis, (iii) EEG artifact analysis and (iv) optimization of the ratio between artifact-free EEG channels and trials to be rejected. The reject criteria were based on a threshold method and on a statistical method evaluating the non-stationarity of the EEG signal (Moretti et al, 2003). Then, two expert electroencephalographists manually confirmed the automatic selections.…”

Section: Off-line Preliminary Data Analysismentioning

confidence: 99%

Human cortical EEG rhythms during long-term episodic memory task. A high-resolution EEG study of the HERA model

Babiloni

Carducci

et al. 2004

NeuroImage

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Many recent neuroimaging studies of episodic memory have indicated an asymmetry in prefrontal involvement, with the left prefrontal cortex more involved than the right in encoding, the right more than the left in retrieval (hemispheric encoding and retrieval asymmetry, or HERA model). In this electroencephalographic (EEG) high-resolution study, we studied brain rhythmicity during a visual episodic memory (recognition) task. The theta (4-6 Hz), alpha (6-12 Hz) and gamma (28-48 Hz) oscillations were investigated during a visuospatial long-term episodic memory task including an encoding (ENC) and retrieval (RET) phases. During the ENC phase, 25 figures representing interiors of buildings ("indoor") were randomly intermingled with 25 figures representing landscapes ("landscapes"). Subject's response was given at left ("indoor") or right ("landscapes") mouse button. During the RET phase (1 h later), 25 figures representing previously presented "indoor" pictures ("tests") were randomly intermingled with 25 figures representing novel "indoor" ("distractors"). Again, a mouse response was required. Theta and alpha EEG results showed no change of frontal rhythmicity. In contrast, the HERA prediction of asymmetry was fitted only by EEG gamma responses, but only in the posterior parietal areas. The ENC phase was associated with gamma EEG oscillations over left parietal cortex. Afterward, the RET phase was associated with gamma EEG oscillations predominantly over right parietal cortex. The predicted HERA asymmetry was thus observed in an unexpected location. This discrepancy may be due to the differential sensitivity of neuroimaging methods to selected components of cognitive processing. The strict relation between gamma response and perception suggests that retrieval processes of long-term memory deeply impinged upon sensory representation of the stored material. © 2004 Elsevier Inc. All rights reserved

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“…Electrodes with consistently poor signal quality are typically removed and then recreated using interpolation from the remaining electrodes, which effectively reduces the spatial resolution of the EEG. Therefore, several methods of removing eye and muscle movement artifacts while retaining EEG data have been proposed (Croft and Barry, 2000;Moretti et al, 2003;Schlögl et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection

Nolan

Whelan

Reilly

2010

Journal of Neuroscience Methods

916

701

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Electroencephalogram (EEG) data are typically contaminated with artifacts (e.g., by eye movements). The effect of artifacts can be attenuated by deleting data with amplitudes over a certain value, for example. Independent component analysis (ICA) separates EEG data into neural activity and artifact; once identified, artifactual components can be deleted from the data. Often, artifact rejection algorithms require supervision (e.g., training using canonical artifacts). Many artifact rejection methods are time consuming when applied to high density EEG data. We describe FASTER (Fully Automated Statistical Thresholding for EEG artifact Rejection). Parameters were estimated for various aspects of data (e.g., channel variance) in both the EEG time-series and in the independent components of the EEG: outliers were detected and removed. FASTER was tested on both simulated EEG (n=47) and real EEG (n=47) data on 128-, 64-, and 32-scalp electrode arrays. Faster was compared to supervised artifact detection by experts and to a variant of the Statistical Control for Dense Arrays of Sensors (SCADS) method. FASTER had > 90% sensitivity and specificity for detection of contaminated channels, eye movement and EMG artifacts, linear trends and white noise.FASTER generally had > 60% sensitivity and specificity for detection of contaminated epochs, vs. 0.15% for SCADS. The variance in the ERP baseline, a measure of noise, was significantly lower for FASTER than either the supervised or SCADS methods. ERP amplitude did not differ significantly between FASTER and the supervised approach.

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Computerized processing of EEG–EOG–EMG artifacts for multi-centric studies in EEG oscillations and event-related potentials

Cited by 247 publications

References 55 publications

Individual analysis of EEG frequency and band power in mild Alzheimer's disease

Individual analysis of EEG frequency and band power in mild Alzheimer's disease

Human cortical EEG rhythms during long-term episodic memory task. A high-resolution EEG study of the HERA model

FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection

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