Klaus-Robert Müller

TL;DR: The neurovascular relationship during periods of spontaneous activity is explored by using temporal kernel canonical correlation analysis (tkCCA), a multivariate method that can take into account any features in the signals that univariate analysis cannot and represent the first multivariate analysis of intracranial electrophysiology and high-resolution fMRI.

TL;DR: Data from a large-scale screening study conducted on 80 novice participants with the Berlin BCI system and its standard machine-learning approach were investigated to understand this phenomenon in Sensorimotor Rhythm (SMR) based BCIs.

TL;DR: An explainable machine-learning approach for the integrated profiling of morphological, molecular and clinical features from breast cancer histology allows for the robust detection of cancer cells and tumour-infiltrating lymphocytes in histological images, and allows assessment of the link between morphological and molecular cancer properties.

TL;DR: This work presents a statistical modeling of aqueous solubility based on measured data, using a Gaussian Process nonlinear regression model (GPsol), and shows that the developed model achieves much higher accuracy than available commercial tools for the prediction ofsolubility of electrolytes.

TL;DR: This work investigates the selection of EEG channels in a BCI that uses the popular CSP algorithm in order to classify voluntary modulations of sensorimotor rhythms (SMR), and finds a setting with 22 channels centered over the motor areas to be the best.