# Online adaptation of a c-VEP Brain-computer Interface(BCI) based on error-related potentials and unsupervised learning

Article · Spüler M, Rosenstiel W, Bogdan M

- Archive id: doi:10.1371/journal.pone.0051077
- Published: 2012-12-07
- Venue: PloS one
- DOI: https://doi.org/10.1371/journal.pone.0051077
- PMID: 23236433
- PMCID: PMC3517594
- Citations: 91
- Source URL: https://doi.org/10.1371/journal.pone.0051077
- PDF: https://europepmc.org/articles/PMC3517594/pdf
- Provenance: europepmc

## 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.

_Abstract reproduced under CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)._

Topics: [Brain-Computer Interfaces](https://space-kitty.com/topics/brain-computer-interfaces)

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