PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Predicting BCI performance to study BCI illiteracy
Benjamin Blankertz, Claudia Sanelli, Sebastian Halder, Eva-Maria Hammer, Andrea Kübler, Klaus-Robert Müller, Gabriel Curio and Thorsten Dickhaus
In: 7th NFSI & ICBEM 2009(2009).

Abstract

Brain-Computer Interfaces (BCIs) allow a user to control a computer application just by brain activity as acquired, e.g., by electroencephalography (EEG). After 30 years of BCI research, the success of BCI control that may be provided still greatly varies between subjects. For a percentage of about 20% the obtained accuracy does not reach the level criterion, meaning that BCI control is not accurate enough to control an application. The development of predictors of BCI performance serves two purposes: a better under-standing of the 'illiterates phenomenon', and avoidance of a costly and frustrating training procedure for subjects 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 subjects in their first session with the Berlin Brain-Computer Interface (BBCI) system which operates on modulations of sensory motor rhythms (SMRs).

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:6446
Deposited By:Stefan Haufe
Deposited On:08 March 2010