The Berlin Brain-Computer Interface: Machine Learning-Based Detection of User Specific Brain States

The Berlin Brain-Computer Interface (BBCI) project develops an EEG-based BCI system that uses machine learning techniques to adapt to the specific brain signatures of each user. This concept allows to achieve high quality feedback already in the very first session without subject training. Here we present the broad range of investigations and experiments that have been performed within the BBCI project. The first kind of experiments analyzes the predictability of performing limbs from the premovement (readiness) potentials including successful feedback experiments. The limits with respect to the spatial resolution of the somatotopy are explored by contrasting brain patterns of movements of (1) left vs. right foot, (2) index vs. little finger within one hand, and (3) finger vs. wrist vs. elbow vs. shoulder within one arm. A study of phantom movements of patients with traumatic amputations shows the potential applicability of this BCI approach. In a complementary approach, voluntary modulations of sensorimotor rhythms caused by motor imagery (left hand vs. right hand vs. foot) are translated into a proportional feedback signal. We report results of a recent feedback study with six healthy subjects with no or very little experience with BCI control: Half of the subjects achieved an information transfer rate above 35 bits per minute (bpm). Furthermore, one subject used the BBCI to operate a mental typewriter in free spelling mode. The overall spelling speed was 4.5 letters per minute including the time needed for the correction errors. These results are encouraging for an EEG-based BCI system in untrained subjects that is independent of peripheral nervous system activity and does not rely on evoked potentials.