Klaus-Robert Müller

TL;DR: In this article, a convergent paradigm is proposed to study brain dynamics, focusing on a single brain site to observe the average effect of stimulating each of many other brain sites, which can be applied generically to explore the diverse milieu of interactions that comprise the connectome.

TL;DR: How neuroimaging data exhibit subclass labels that may contain valuable information is illustrated and a novel method that allows subclass labels to be incorporated efficiently into the classifier is introduced, called Relevance Subclass LDA (RSLDA), which computes an individual classification hyperplane for each subclass.

TL;DR: This paper introduces two graph-based preprocessing techniques, which adapt the original TCRFR for extremely weakly supervised scenarios, and outperforms the previous automatic estimation methods on synthetic data and provides a comparable result to the manual labored, time-consuming geostatistics approach on real data.

TL;DR: A novel approach for a) measuring label noise and b) removing structured label noise is presented and its usefulness for EEG data analysis is demonstrated using a standard d2 test for visual attention.

TL;DR: In this article, the authors use resampling methods to assess the quality of the discovered projections and show experimentally that their proposed variance estimations are strongly correlated with the separation error, and demonstrate that this reliability estimation can be used to choose the appropriate ICA-model, to enhance significantly the separation performance.