FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection

@article{Nolan2010FASTER,
  title={
 FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection},
  author={Hugh Nolan and Robert A. Whelan and R. B. Reilly},
  journal={Journal of Neuroscience Methods},
  year={2010},
  volume={192},
  pages={152-162}
}

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References

SHOWING 1-10 OF 22 REFERENCES
Extended ICA Removes Artifacts from Electroencephalographic Recordings
TLDR
The results show that ICA can effectively detect, separate and remove activity in EEG records from a wide variety of artifactual sources, with results comparing favorably to those obtained using regression-based methods.
Automatic Removal of Artifacts from EEG Data Using ICA and Exponential Analysis
TLDR
This paper proposes an automatic artifacts removal scheme for EEG data by combining ICA and exponential analysis, and demonstrates that the proposed scheme for artifacts removal has excellent performance.
Removing electroencephalographic artifacts by blind source separation.
TLDR
The results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods.
AUTOMATIC REMOVAL OF ARTIFACTS FROM EEG DATA USING ICA AND NONLINEAR EXPONENTIAL ANALYSIS
TLDR
Experimental results demonstrate that the proposed scheme for artifacts removal has excellent performances for detecting and removing these artifacts from EEG data.
Statistical control of artifacts in dense array EEG/MEG studies.
TLDR
This work proposes a procedure for statistical correction of artifacts in dense array studies (SCADS), which detects individual channel artifacts using the recording reference, detects globalartifacts using the average reference, replaces artifact-contaminated sensors with spherical interpolation statistically weighted on the basis of all sensors, and computes the variance of the signal across trials to document the stability of the averaged waveform.
Automatic removal of the eye blink artifact from EEG using an ICA-based template matching approach.
TLDR
Objective evaluation of the real results shows that the proposed algorithm can remove the eye blink artifact from the EEG while causing little distortion to the underlying brain activities.
Computerized processing of EEG-EOG-EMG artifacts for multi-centric studies in EEG oscillations and event-related potentials.
  • D. Moretti, F. Babiloni, C. Babiloni
  • Computer Science
    International journal of psychophysiology : official journal of the International Organization of Psychophysiology
  • 2003
Information-based modeling of event-related brain dynamics.
...
...