TY - JOUR
T1 - The Berlin brain - computer interface
T2 - Accurate performance from first-session in BCI-naïve subjects
AU - Blankertz, Benjamin
AU - Losch, Florian
AU - Krauledat, Matthias
AU - Dornhege, Guido
AU - Curio, Gabriel
AU - Müller, Klaus Robert
N1 - Funding Information:
Manuscript received September 10, 2007; revised January 26, 2008. First published June 10, 2008; current version published September 26, 2008. This work was supported in part by the Bundesministerium für Bildung und Forschung (BMBF) under Grant FKZ 01IBE01A/B and in part by the Information Society Technologies (IST) Programme of the European Community under the PASCAL Network of Excellence, IST-2002-506778. This publication only reflects the authors’ views. Asterisk indicates corresponding author.
PY - 2008/10
Y1 - 2008/10
N2 - 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. Müller, 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-naïve 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.
AB - 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. Müller, 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-naïve 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.
KW - Brain-computer interface
KW - Common spatial pattern analysis
KW - Electroencephalography
KW - Event-related desynchronization
KW - Machine learning
KW - Pattern classification
KW - Sensorymotor rhythms
KW - Single-trial analysis
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U2 - 10.1109/TBME.2008.923152
DO - 10.1109/TBME.2008.923152
M3 - Article
C2 - 18838371
AN - SCOPUS:44449127606
VL - 55
SP - 2452
EP - 2462
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
SN - 0018-9294
IS - 10
M1 - 18
ER -