The Berlin Brain-Computer Interface: Accurate performance from first-session in BCI-naive subjects

• Published in: IEEE transactions on biomedical engineering Vol. 55; no. 10; pp. 2452 - 2462
• Main Authors: Blankertz, Benjamin, Losch, Florian, Krauledat, Matthias, Dornhege, Guido, Curio, Gabriel, Muller, Klaus-Robert
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2008; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Artificial Intelligence; Biofeedback, Psychology; Brain - physiology; Brain Mapping; Brain-computer interface; common spatial pattern analysis; Control systems; Data analysis; Electroencephalography; Electromyography; Electrooculography; event-related desynchronization; Evoked Potentials, Visual; Female; Foot; Foot - physiology; Functional Laterality; Hand - physiology; Humans; Imagination - physiology; Learning - physiology; Learning systems; Machine learning; Male; Man-Machine Systems; Movement - physiology; Neurofeedback; Pattern analysis; pattern classification; Pattern Recognition, Automated; Performance analysis; Psychomotor Performance - physiology; Rhythm; sensorymotor rhythms; Signal Processing, Computer-Assisted; single-trial analysis; Studies; User-Computer Interface
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2008.923152

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.