Klaus-Robert Müller

TL;DR: A convolutional neural network (CNN) is contributed for the robust classification of a steady-state visual evoked potentials (SSVEPs) paradigm for a brain-controlled exoskeleton under ambulatory conditions in which numerous artifacts may deteriorate decoding.

TL;DR: Clustered FL (CFL) as discussed by the authors exploits geometric properties of the FL loss surface to group the client population into clusters with jointly trainable data distributions, which can be viewed as a postprocessing method that will always achieve greater or equal performance than conventional FL by allowing clients to arrive at more specialized models.

TL;DR: This work defines features based on a variant of the common spatial patterns (CSP) algorithm that are constructed invariant with respect to such nonstationarities such as disturbance covariance matrices from fluctuations in visual processing.

TL;DR: This work presents Deep SAD, an end-to-end deep methodology for general semi-supervised anomaly detection, and introduces an information-theoretic framework for deep anomaly detection based on the idea that the entropy of the latent distribution for normal data should be lower than the entropy the anomalous distribution, which can serve as a theoretical interpretation for the method.

TL;DR: This contribution introduces the Berlin Brain–Computer Interface (BBCI) and presents setups where the user is provided with intuitive control strategies in plausible gaming applications that use biofeedback.