Boosting Bit Rates and Error Detection for the Classification of Fast-paced Motor Commands Based on Single-trial EEG Analysis

Brain-Computer-Interfaces (BCI) involve two coupled adapting systems: the human subject and the computer. In developing our BCI, our goal was to minimize the need for subject training and to impose the major learning load on the computer. To this end, we use behavioral paradigms that exploit single-trial EEG potentials preceding voluntary nger movements. Here, we report recent results on the basic physiology of such pre-movement event-related potentials (ERP): 1) We predict the laterality of imminent left vs. right hand nger movements in a natural keyboard typing condition and demonstrate that a single-trial classi- cation based on the lateralized Bereitschaftspotential (BP) achieves good accuracies even at a pace as fast as 2 taps per second. Results for 4 out of 8 subjects reached a peak information transfer rate of more than 15 bits per minute (bpm); the 4 other subjects reached 6-10 bpm. 2) We detect cerebral error potentials from single false-response trials in a forced-choice task, reecting the subject's recognition of an erroneous response. Based on a specically tailored classi cation procedure that limits the rate of false positives at, e.g. 2 %, the algorithm manages to detect 85 % of error trials in 7/8 subjects. Thus, concatenating a primary single-trial BP-paradigm involving nger classication feedback with such secondary error detection could serve as an ecient on-line conrmation/correction tool for improvement of bit rates in a future BCI setting. As the present variant of the Berlin BCI (BBCI) is designed to achieve fast classications in normally behaving subjects, it opens a new perspective for assistance of action control in time-critical behavioral contexts; the potential transfer to paralysed patients will require further study