Recovering EEG brain signals: artifact suppression with wavelet enhanced independent component analysis.

By Nazareth P. Castellanos, Valeri A. Makarov. Journal of Neuroscience Methods, 2006

558 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/6d03dce3f07614da34a34d72db6e7ad465aa9ff8

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Citations in the corpus

The Harvard Automated Processing Pipeline for Electroencephalography (HAPPE): Standardized Processing Software for Developmental and High-Artifact Data (2018, methodology): “…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).”
EEG artifact removal—state-of-the-art and guidelines (2015, methodology): “Using a combination of algorithms to remove artifacts from the EEG is an option that has gained attention in the recent literature for multi-channel [55, 132, 133] and single-channel processing [57, 58, 126].”
Brain Computer Interfaces, a Review (2012, background): “term n(t) from Equation (4), by assuming that the observed data is noiseless or that the noise is too weak for consideration [160,161].”
Consumer neuroscience: Assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking (2013, methodology): “Various approaches combining ICA with wavelet denoising have been proposed in the literature proving the efficiency of this combination (Akhtar et al., 2012; Castellanos & Makarov, 2012; Aminghafari, Cheze, & Poggi, 2006; Ren, Yan, Wang, & Hu, 2006; Vazqueza et al., 2012).”
Decomposing Spatiotemporal Brain Patterns into Topographic Latent Sources (2014, unknown): “Eye-movement artifacts were corrected using a wavelet independent components analysis method (WICA) that has proven to be quite robust at removing eye artifacts without introducing spurious correlations between the electrodes (Castellanos and Makarov, 2006).”
RELAX‐Jr: An Automated Pre‐Processing Pipeline for Developmental EEG Recordings (2024, unknown): “…(Bailey, Biabani, et al. 2023; Bailey, Hill, et al. 2023), which implements multi-channel Wiener filters (MWF) (Borowicz 2018; Somers, Francart, and Bertrand 2018) and/ or wavelet-enhanced independent component analysis (wICA) (Castellanos and Makarov 2006) to reduce or remove arte-factual signals.”
The developmental trajectory of functional excitation‐inhibition balance relates to language abilities in autistic and allistic children (2023, methodology): “Artifacts were removed with wavelet-enhanced independent component analysis (Castellanos & Makarov, 2006) with a threshold of 3.”