Michael Tangermann

TL;DR: This study shows how to design and train convolutional neural networks to decode task‐related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG‐based brain mapping.

TL;DR: This paper focuses on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT) and identifies four application areas where disabled individuals could greatly benefit from advancements inBCI technology, namely, “Communication and Control”, ‘Motor Substitution’, ”Entertainment” and “Motor Recovery”.

TL;DR: The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide high quality neuroscientific data for open access to the scientific community and it is the hope that winning entries may enhance the analysis methods of future BCIs.

TL;DR: This work proposes a universal and efficient classifier of ICA components for the subject independent removal of artifacts from EEG data that is applicable for different electrode placements and supports the introspection of results.

TL;DR: An outline of the Berlin brain-computer interface (BBCI) is given, which can be operated with minimal subject training, and spelling with the novel BBCI-based Hex-o-Spell text entry system, which gains communication speeds of 6-8 letters per minute.