publication . Article . 2016

Sparse time artifact removal

Alain de Cheveigné;
Open Access English
  • Published: 08 Jan 2016 Journal: Journal of Neuroscience Methods, volume 262, pages 14-20 (issn: 01650270, Copyright policy)
Abstract Background Muscle artifacts and electrode noise are an obstacle to interpretation of EEG and other electrophysiological signals. They are often channel-specific and do not fully benefit from component analysis techniques such as ICA, and their presence reduces the dimensionality needed by those techniques. Their high-frequency content may mask or masquerade as gamma band cortical activity. New method The sparse time artifact removal (STAR) algorithm removes artifacts that are sparse in space and time. The time axis is partitioned into an artifact-free and an artifact-contaminated part, and the correlation structure of the data is estimated from the cova...
Persistent Identifiers
free text keywords: General Neuroscience, EEG, MEG, LFP, ECoG, Artifact, Myogenic, ICA, Sensor noise, Neuroscience(all), Component analysis, Covariance matrix, Correlation, Pattern recognition, Subspace topology, Interpolation, Computer vision, Linear component, Communication channel, Artificial intelligence, business.industry, business, Curse of dimensionality, Computer science
Funded by
Cognitive Control of a Hearing Aid
  • Funder: European Commission (EC)
  • Project Code: 644732
  • Funding stream: H2020 | RIA
Validated by funder
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