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We propose to fuse two currently separate research lines on novel therapies for stroke rehabilitation: brain-computer interface (BCI) training and transcranial electrical stimulation (TES). Specifically, we show that BCI technology can be used to learn personalized decoding models that relate the global configuration of brain rhythms in individual subjects (as measured by EEG) to their motor performance...
We describe a modular framework for video frame prediction. We refer to it as a Flexible Spatio-Temporal Network (FSTN) as it allows the extrapolation of a video sequence as well as the estimation of synthetic frames lying in between observed frames and thus the generation of slow-motion videos. By devising a customized objective function comprising decoding, encoding, and adversarial losses, we are...
In this paper, an approach for learning optimal striking points is proposed. Based on a ball-flight model and a rebound model, a set of reachable striking points within the robot's workspace can be obtained. However, while these striking points are geometrically reachable, their success probability differs substantially due to the robot's nonlinear dynamics, the distance to the ball, the need to reach...
Screws and gears are a source of periodically recurring nonlinear effects in mechanical dynamical systems. Unless the state sampling frequency is much higher than the periodic effect, model-free controllers cannot always compensate these effects, and good physical models for such periodic dynamics are challenging to construct. We investigate nonparametric system identification with an explicit focus...
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