Citation

Blankertz B, Sannelli C, Halder S, Hammer EM, Kübler A, Müller KR, Curio G, Dickhaus T. Neuroimage 2010; 51(4): 1303-1309.

Affiliation

Machine Learning Laboratory, Berlin Institute of Technology, Germany; Intelligent Data Analysis Group, Fraunhofer FIRST, Berlin, Germany.

Copyright

(Copyright © 2010, Elsevier Publishing)

DOI

10.1016/j.neuroimage.2010.03.022

PMID

20303409

Abstract

Brain-Computer Interfaces (BCIs) allow a user to control a computer application by brain activity as measured, e.g., by electroencephalography (EEG). After about 30 years of BCI research, the success of control that is achieved by means of a BCI system still greatly varies between subjects. For about 20% of potential users the obtained accuracy does not reach the level criterion, meaning that BCI control is not accurate enough to control an application. The determination of factors that may serve to predict BCI performance, and the development of methods to quantify a predictor value from psychological and/or physiological data serves two purposes: a better understanding of the 'BCI-illiteracy phenomenon', and avoidance of a costly and eventually frustrating training procedure for participants who might not obtain BCI control. Furthermore, such predictors may lead to approaches to antagonize BCI-illiteracy. Here, we propose a neurophysiological predictor of BCI performance which can be determined from a two minutes recording of a 'relax with eyes open' condition using two Laplacian EEG channels. A correlation of r = 0.53 between the proposed predictor and BCI feedback performance was obtained on a large data base with N = 80 BCI-naive participants in their first session with the Berlin Brain-Computer Interface (BBCI) system which operates on modulations of sensory motor rhythms (SMRs).

Language: en