Automatic classification of artifactual ICA-components for artifact removal in EEG signals.

ABSTRACT:

Background

Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI) or for Mental State Monitoring). While hand-optimized selection of source components derived from Independent Component Analysis (ICA) to clean EEG data is widespread, the field could greatly profit from automated solutions based on Machine Learning methods. Existing ICA-based removal strategies depend on explicit recordings of an individual's artifacts or have not been shown to reliably identify muscle artifacts.

Methods

We propose an automatic method for the classification of general artifactual source components. They are estimated by TDSEP, an ICA method that takes temporal correlat

SUBMITTER: Winkler I 

PROVIDER: S-EPMC3175453 | biostudies-literature | 2011 Aug

REPOSITORIES: biostudies-literature