The Berlin Brain--Computer Interface: accurate performance from first-session in BCI-naïve subjects.
The 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.
Blankertz
Benjamin
B
Machine Learning Laboratory, Technical University of Berlin, Berlin, Germany. blanker@cs.tu-berlin.de
Losch
Florian
F
Krauledat
Matthias
M
Dornhege
Guido
G
Curio
Gabriel
G
Müller
Klaus-Robert
KR
D000328:Adult; D001185:Artificial Intelligence; D001676:Biofeedback, Psychology; D001921:Brain / Q000502:physiology; D001931:Brain Mapping; D004569:Electroencephalography; D004576:Electromyography; D004585:Electrooculography; D005074:Evoked Potentials, Visual; D005260:Female; D005528:Foot / Q000502:physiology; D007839:Functional Laterality; D006225:Hand / Q000502:physiology; D006801:Humans; D007092:Imagination / Q000502:physiology; D007858:Learning / Q000502:physiology; D008297:Male; D008328:Man-Machine Systems; D009068:Movement / Q000502:physiology; D010363:Pattern Recognition, Automated; D011597:Psychomotor Performance / Q000502:physiology; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface