Online Adaptation of a c-VEP Brain-Computer Interface(BCI) Based on Error-Related Potentials and Unsupervised Learning

• Published in: PloS one Vol. 7; no. 12; p. e51077
• Main Authors: Spüler, Martin, Rosenstiel, Wolfgang, Bogdan, Martin
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 07.12.2012; Public Library of Science (PLoS)
• Subjects: Adaptation; Adult; Biology; Brain; Brain - physiology; Brain-Computer Interfaces; Calibration; Communication; Computer applications; Computer engineering; Computer Science; Electroencephalography; Engineering; Error detection; Evoked Potentials, Visual - physiology; Feasibility studies; Female; Human-computer interface; Humans; Implants; Information transfer; Internet; Learning - physiology; Male; Medicine; Neural networks; On-line systems; Personal computers; Rehabilitation; Unsupervised learning; User-Computer Interface; Visual evoked potentials; Germany
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0051077

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.