A new ICA-based fingerprint method for the automatic removal of physiological artifacts from EEG recordings.

ABSTRACT:

Background

EEG may be affected by artefacts hindering the analysis of brain signals. Data-driven methods like independent component analysis (ICA) are successful approaches to remove artefacts from the EEG. However, the ICA-based methods developed so far are often affected by limitations, such as: the need for visual inspection of the separated independent components (subjectivity problem) and, in some cases, for the independent and simultaneous recording of the inspected artefacts to identify the artefactual independent components; a potentially heavy manipulation of the EEG signals; the use of linear classification methods; the use of simulated artefacts to validate the methods; no testing in dry electrode or high-density EEG datasets; applications limited to specific conditions

SUBMITTER: Tamburro G 

PROVIDER: S-EPMC5826009 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature