Robustifying EEG data analysis by removing outliers
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.