PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Classification of artifactual ICA components
Michael Tangermann, Irene Winkler, Stefan Haufe and Benjamin Blankertz
Int J Bioelectromagnetism Volume 11, Number 2, pp. 110-114, 2009.


The analysis of EEG signals for the use in BCI systems and for mental state monitoring applications is often impeded by artifacts caused by muscular activity or external technical sources. A promising approach for the reduction or removal of artifacts is based on methods of Blind Source Separation (BSS), which transform the original EEG signal into independent source components. In order to avoid the time-consuming hand rating of sources into artifactual and non-artifactual components, an automated method for their classification is proposed. Applying state of the art machine learning algorithms and nonlinear classification with a Support Vector Machine (SVM), the automated method shows a high level of agreement (90.5%) on unseen data with ratings of human experts.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:6481
Deposited By:Stefan Haufe
Deposited On:08 March 2010