• Benjamin Blankertz, Florian Losch, Matthias Krauledat, Guido Dornhege, Gabriel Curio, and Klaus-Robert Müller.
• Machine Learning Laboratory, Technical University of Berlin, Berlin, Germany. blanker@cs.tu-berlin.de
• IEEE Trans Biomed Eng. 2008 Oct 1; 55 (10): 2452-62.

AbstractThe Berlin Brain--Computer Interface (BBCI) project develops a noninvasive BCI system whose key features are: 1) the use of well-established motor competences as control paradigms; 2) high-dimensional features from multichannel EEG; and 3) advanced machine-learning techniques. Spatio-spectral changes of sensorimotor rhythms are used to discriminate imagined movements (left hand, right hand, and foot). A previous feedback study [M. Krauledat, K.-R. MUller, and G. Curio. (2007) The non-invasive Berlin brain--computer Interface: Fast acquisition of effective performance in untrained subjects. NeuroImage. [Online]. 37(2), pp. 539--550. Available: http://dx.doi.org/10.1016/j.neuroimage.2007.01.051] with ten subjects provided preliminary evidence that the BBCI system can be operated at high accuracy for subjects with less than five prior BCI exposures. Here, we demonstrate in a group of 14 fully BCI-naIve subjects that 8 out of 14 BCI novices can perform at >84% accuracy in their very first BCI session, and a further four subjects at >70%. Thus, 12 out of 14 BCI-novices had significant above-chance level performances without any subject training even in the first session, as based on an optimized EEG analysis by advanced machine-learning algorithms.

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