PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

The Berlin Brain-Computer Interface: Accurate performance from first-session in BCI-naive subjects
Benjamin Blankertz, Florian Losch, Matthias Krauledat, Guido Dornhege, Gabriel Curio and Klaus-Robert Müller
IEEE Trans Biomed Eng Volume 55, Number 10, pp. 2452-2462, 2008.

Abstract

The Berlin Brain-Computer Interface (BBCI) project develops a non-invasive BCI system whose key features are (1) the use of well-established motor competences as control paradigms, (2) high-dimensional features from multi-channel EEG and (3) advanced machine learning techniques. Spatio- spectral changes of sensorimotor rhythms are used to discrimi- nate imagined movements (left hand, right hand, foot). A previous feedback study with 10 subjects provided preliminary evidence that the BBCI system can be operated at high accuracy for subjects with less than 5 prior BCI exposures. Here, we demonstrate in a group of 14 fully BCI-naïve subjects that 8/14 BCI novices can perform at >84% accuracy in their very first BCI session, and a further 4 subjects >70%. Thus, 12/14 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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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:3873
Deposited By:Benjamin Blankertz
Deposited On:25 February 2008