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 serve 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 minute 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).
Bibliographical noteFunding Information:
This work was supported in part by grants from the Deutsche Forschungsgemeinschaft (DFG) , MU 987/3-1 and KU 1453-1 , Bundesministerium für Bildung und Forschung (BMBF) , FKZ 01IB001A/B and 01GQ0850 and by the FP7-ICT Programme of the European Community, under the PASCAL2 Network of Excellence , ICT-216886 . This publication only reflects the authors' views. Funding agencies are not liable for any use that may be made of the information contained herein.
- BCI illiteracy
- Brain-computer interface (BCI)
- Event-related desynchronization (ERD)
- Neurophysiological predictor
- Sensory motor rhythms (SMRs)
ASJC Scopus subject areas
- Cognitive Neuroscience