Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis

By Arnaud Delorme, Terrence J. Sejnowski, Scott Makeig. NeuroImage, 2007

1,744 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/6f53a8be6db8cec65991d943840af46fce95aa7c

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Citations in the corpus

EEG Source Imaging: A Practical Review of the Analysis Steps (2019, methodology): “The ICA is used to detect and correct artifacts, particular eye movements, eye blinks and cardiac pulse artifacts (43).”
ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features. (2011, background): “Their time course is well captured by the kurtosis (Barbati et al., 2004; Delorme et al., 2007), a measure that is very sensitive to outliers in the amplitude distribution.”
The Harvard Automated Processing Pipeline for Electroencephalography (HAPPE): Standardized Processing Software for Developmental and High-Artifact Data (2018, methodology): “This combination has previously been used with developmental EEG data (Delorme et al., 2007; Piazza et al., 2016).”
Independent EEG Sources Are Dipolar (2012, methodology): “In particular, components produced by ICA decompositions that minimize mutual information between simultaneously recorded signal values have not been much compared to components produced by BSS algorithms that simultaneously minimize component signal redundancy at multiple time delays [16,21].”
Electrocortical activity is coupled to gait cycle phase during treadmill walking (2011, positive): “Independent component analysis (ICA) combined with magnetic resonance based head models can be used to overcome electromyographic, electroocular, movement artifact, and line noise contamination of EEG signals (Delorme and Makeig, 2004; Delorme et al., 2007; Gwin et al., 2010; Jung et al., 2000a,b; M”
Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review (2021, methodology): “Previous literature [13, 14, 61] investigated methods for identiﬁcation and elimination of MI-EEG artifacts, which will not be repeated in this paper.”
Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis (2017, methodology): “The ICA method is based only on statistical independence between source signals [21]–[24].”
High-frequency brain activity and muscle artifacts in MEG/EEG: a review and recommendations (2013, methodology): “In ICA, the spatial filters are derived by producing the set of maximally temporally independent signals in the MEG/EEG data (Delorme et al., 2007).”
Diagnosis of Alzheimer's disease from EEG signals: where are we standing? (2010, methodology): “, both EEG and artifacts may be described by a limited number of sources, seem to good approximation to be fulfilled in the case of EEG recordings [21, 24].”
Human neuronal excitation/inhibition balance explains and predicts neurostimulation induced learning benefits (2023, methodology): “EEG segments that contained artefacts that could not be removed by ICA were visually inspected and rejected from the analysis (Delorme et al., 2007).”
Child and adolescent development of the brain oscillatory activity during a working memory task. (2023, methodology): “Independent components were identified by visual inspection (Delorme et al., 2007).”