The Berlin brain - computer interface: Accurate performance from first-session in BCI-naïve subjects

Benjamin Blankertz; Florian Losch; Matthias Krauledat; Guido Dornhege; Gabriel Curio; Klaus Robert Müller

doi:10.1109/TBME.2008.923152

The Berlin brain - computer interface: Accurate performance from first-session in BCI-naïve subjects

Benjamin Blankertz, Florian Losch, Matthias Krauledat, Guido Dornhege, Gabriel Curio, Klaus Robert Müller

Research output: Contribution to journal › Article › peer-review

267 Citations (Scopus)

Abstract

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.

Original language	English
Article number	18
Pages (from-to)	2452-2462
Number of pages	11
Journal	IEEE Transactions on Biomedical Engineering
Volume	55
Issue number	10
DOIs	https://doi.org/10.1109/TBME.2008.923152
Publication status	Published - 2008 Oct

Bibliographical note

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.

Keywords

Brain-computer interface
Common spatial pattern analysis
Electroencephalography
Event-related desynchronization
Machine learning
Pattern classification
Sensorymotor rhythms
Single-trial analysis

ASJC Scopus subject areas

Biomedical Engineering

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10.1109/TBME.2008.923152

Cite this

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title = "The Berlin brain - computer interface: Accurate performance from first-session in BCI-na{\"i}ve subjects",

abstract = "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. M{\"u}ller, 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-na{\"i}ve 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.",

keywords = "Brain-computer interface, Common spatial pattern analysis, Electroencephalography, Event-related desynchronization, Machine learning, Pattern classification, Sensorymotor rhythms, Single-trial analysis",

author = "Benjamin Blankertz and Florian Losch and Matthias Krauledat and Guido Dornhege and Gabriel Curio and M{\"u}ller, {Klaus Robert}",

note = "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 f{\"u}r 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{\textquoteright} views. Asterisk indicates corresponding author.",

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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.

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The Berlin brain - computer interface: Accurate performance from first-session in BCI-naïve subjects

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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