A Brain-Computer Interface (BCI) is a novel augmentative communication system that translates human intentions - reflected by suitable brain signals - into a control signal for an output device such as a computer application or a neuroprosthesis. The crucial point is that the system works without using the normal output pathways of peripheral nerves and muscles, just the manifestation of thought. In developing a BCI system many fields of research are involved, such as classification, signal processing, neurophysiology, measurement technology, psychology, control theory. Since the 1970s several labs succeeded in building BCI systems that can provide feedback control from thoughts. The classical approach is to establish EEG-based control by setting up a system that is controlled by a specific EEG feature which is known to be susceptible to conditioning and to let the subjects learn the voluntary control of that feature, a process that may well require several weeks or month. In recent years a machine learning approach to BCI was pioneered by Fraunhofer FIRST Berlin (NIPS*01) and the IDIAP Institute Martigny in which the system adapts to the individual brain signatures of each user, thereby drastically reducing or even eliminating the need for subject training. Still we are faced with a lot of problems, e.g., the dramatic inter-subject variability in performance and the question how to adapt classifiers online to cope with the changing characteristics of background activity. Furthermore several labs started to investigate Brain-Computer Interfaces that use brain signals recorded invasively from the surface of the cortex or from inside the brain. Due to the challenging problems in the design of the BCI control it is of uttermost necessity to bring together researchers from all involved fields to discuss possible solutions and new directions of research.