title: Online adaptation of a c-VEP Brain-computer Interface(BCI) based on error-related potentials and unsupervised learning.
abstract: The goal of a Brain-Computer Interface (BCI) is to control a computer by pure brain activity. Recently, BCIs based on code-modulated visual evoked potentials (c-VEPs) have shown great potential to establish high-performance communication. In this paper we present a c-VEP BCI that uses online adaptation of the classifier to reduce calibration time and increase performance. We compare two different approaches for online adaptation of the system: an unsupervised method and a method that uses the detection of error-related potentials. Both approaches were tested in an online study, in which an average accuracy of 96% was achieved with adaptation based on error-related potentials. This accuracy corresponds to an average information transfer rate of 144 bit/min, which is the highest bitrate reported so far for a non-invasive BCI. In a free-spelling mode, the subjects were able to write with an average of 21.3 error-free letters per minute, which shows the feasibility of the BCI system in a normal-use scenario. In addition we show that a calibration of the BCI system solely based on the detection of error-related potentials is possible, without knowing the true class labels.
authors.affiliation: Wilhelm-Schickard-Institute for Computer Science, University of Tübingen, Tübingen, Germany. spueler@informatik.uni-tuebingen.de
authors.forename: Martin
authors.initials: M
authors.lastname: Spüler
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Rosenstiel
authors.forename: Martin
authors.initials: M
authors.lastname: Bogdan
mesh_terms: D000328:Adult; D001921:Brain / Q000502:physiology*; D062207:Brain-Computer Interfaces; D004569:Electroencephalography; D005074:Evoked Potentials, Visual / Q000502:physiology*; D005260:Female; D006801:Humans; D007858:Learning / Q000502:physiology*; D008297:Male; D014584:User-Computer Interface