The Berlin brain-computer interface

Brain-Computer Interface (BCI) systems establish a direct communication channel from the brain to an output device. These systems use brain signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications. BCI systems that bypass conventional motor output pathways of nerves and muscles can provide novel control options for paralyzed patients. The classical approach to establish EEG-based control is to set 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. In contrast, the Berlin Brain-Computer Interface (BBCI) uses well established motor competences in control paradigms and a machine learning approach to extract subject-specific discriminability patterns from high-dimensional features. Thus the long subject training is replaced by a short calibration measurement (20 minutes) and machine training (1 minute). We report results from a study with six subjects who had no or little experience with BCI feedback. The experiment encompassed three kinds of feedback that were all controlled by voluntary brain signals, independent from peripheral nervous system activity and without resorting to evoked potentials. Two of the feedback protocols were asynchronous and one was synchronous (i.e., commands can only be emitted synchronously with an external pace). The information transfer rate in the best session was above 35 bits per minute (bpm) for 3 subjects, above 24 and 15 bpm for further two subjects, while one subject could achieve no BCI control. Compared to other BCI systems which need longer subject training to achieve comparable results we believe that the key to success in the BBCI system is its flexibility due to complex features and its adaptivity which respects the enormous inter-subject variability.