FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection.

By H. Nolan, Robert A. Whelan, Richard B. Reilly. Journal of Neuroscience Methods, 2010

1,044 citations recorded by the source corpus.

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. FASTER also aggregates the ERP across subject datasets, and detects outlier datasets. 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.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/d095d3669f238521c7d7b2113ab88e8e28916b86

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Citations in the corpus

Decoding the tradeoff between encoding and retrieval to predict memory for overlapping events (2018, positive): “We used an automatic artifact correction algorithm based on (Nolan et al., 2010) using Independent Components Analysis (ICA; Bell and Sejnowski, 1995; Onton and Makeig, 2006) to detect and correct for eye blinks and saccades.”
Deep learning-based electroencephalography analysis: a systematic review (2019, background): “Artifact removal techniques usually require the intervention of a human expert [131].”
The Harvard Automated Processing Pipeline for Electroencephalography (HAPPE): Standardized Processing Software for Developmental and High-Artifact Data (2018, methodology): “…software tools automating various stages of EEG processing have become a methodological focus (e.g., PREP, Bigdely-Shamlo et al., 2015, FASTER, Nolan et al., 2010, ADJUST, Mognon et al., 2011, TAPEEG, Hatz et al., 2015, ASR, Mullen et al., 2013, MARA, Winkler et al., 2014, SASICA, Chaumon et…”
EEG artifact removal—state-of-the-art and guidelines (2015, background): “excessive interference [18], it is often desirable to retain as”
The PREP pipeline: standardized preprocessing for large-scale EEG analysis (2015, methodology): “The FASTER pipeline (Nolan et al., 2010) uses a combination of statistical thresholding and ICA to identify and remove contaminated channels and bad epochs, particularly those associated with eye movements, muscle artifacts, trends, and white noise.”
The impact of a monthly unconditional cash transfer on child brain activity: A 4-year follow-up (2026, unknown): “The EEGLAB plug-in FASTER(Nolan et al., 2010) was used to identify bad channels prior to using the standard MADE ICA removal procedure.”
Neural correlates involved in perspective-taking in early childhood (2024, methodology): “Then, artifacted channels identified by FASTER (Fully Automated Statistical Thresholding for EEG artifact Rejection; (Nolan et al., 2010)) were removed.”
Monitoring pilot trainees’ cognitive control under a simulator-based training process with EEG microstate analysis (2024, unknown): “Bad local channels in each segment were detected using FASTER 54 criteria (variance, median gradient, amplitude range, and deviation from mean amplitude) when one or more Z scores of four criteria were greater than 3 standard deviations from the mean, which were interpolated thereafter using spheric”
Neural correlates of processing case in adults and children. (2025, unknown): “The SASICA and FASTER algorithms (Chaumon, Bishop, & Busch, 2015 ; Nolan, Whelan, & Reilly, 2010) were used to identify artifactual independent components.”