PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Robustifying EEG data analysis by removing outliers
Matthias Krauledat, Guido Dornhege, Benjamin Blankertz and Klaus-Robert Müller
Choas and Complexity Letters Volume 2, Number 3, pp. 259-274, 2007.

Abstract

Biomedical signals such as EEG are typically contaminated by measurement artifacts, outliers and non-standard noise sources. We propose to use techniques from robust statistics and machine learning to reduce the influence of such distortions. Two showcase application scenarios are studied: (a) Lateralized Readiness Potential (LRP) analysis, where we show that a robust treatment of the EEG allows to reduce the necessary number of trials for averaging and the detrimental influence of e.g. ocular artifacts and (b) single trial classification in the context of Brain Computer Interfacing, where outlier removal procedures can strongly enhance the classification performance.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:3305
Deposited By:Benjamin Blankertz
Deposited On:07 February 2008