PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Neurophysiological Predictor of SMR-Based BCI Performance
Benjamin Blankertz, Claudia Sannelli, Sebastian Halder, Eva Maria Hammer, Andrea Kübler, Klaus-Robert Müller, Gabriel Curio and Thorsten Dickhaus
NeuroImage 2010. ISSN 1053-8119

Abstract

Brain-Computer Interfaces (BCIs) allow a user to control a computer appli- cation 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).

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:6790
Deposited By:Thorsten Dickhaus
Deposited On:08 March 2010