PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

The non-invasive Berlin Brain-Computer Interface: Fast acquisition of effective performance in untrained subjects
Benjamin Blankertz, Guido Dornhege, Matthias Krauledat, Klaus-Robert Müller and Gabriel Curio
Neuroimage Volume 37, Number 2, pp. 539-550, 2007.


Brain-Computer Interface (BCI) systems establish a direct communication channel from the brain to an output device. These systems use brain signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications. BCI systems that bypass conventional motor output pathways of nerves and muscles can provide novel control options for paralyzed patients. One classical approach to establish EEG-based control is to set up a system that is controlled by a specific EEG feature which is known to be susceptible to conditioning and to let the subjects learn the voluntary control of that feature. In contrast, the Berlin Brain-Computer Interface (BBCI) uses well established motor competences of its users and a machine learning approach to extract subject-specific patterns from high-dimensional features optimized for detecting the user’s intent. Thus the long subject training is replaced by a short calibration measurement (20 minutes) and machine learning (1 minute). We report results from a study in which ten subjects, who had no or little experience with BCI feedback, controlled computer applications by voluntary imagination of limb movements: these intentions led to modula- tions of spontaneous brain activity specifically, somatotopically matched sensorimotor 7-30 Hz rhythms were diminished over pericentral cortices. The peak information transfer rate was above 35 bits per minute (bpm) for 3 subjects, above 23 bpm for two, and above 12 bpm for 3 subjects, while one subject could achieve no BCI control. Compared to other BCI systems which need longer subject training to achieve comparable results we propose that the key to quick efficiency in the BBCI system is its flexibility due to complex but physiologically meaningful features and its adaptivity which respects the enormous inter- subject variability.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:3302
Deposited By:Benjamin Blankertz
Deposited On:07 February 2008