Title |
Online Adaptation of a c-VEP Brain-Computer Interface(BCI) Based on Error-Related Potentials and Unsupervised Learning
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Published in |
PLOS ONE, December 2012
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DOI | 10.1371/journal.pone.0051077 |
Pubmed ID | |
Authors |
Martin Spüler, Wolfgang Rosenstiel, Martin Bogdan |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 4 | 3% |
Germany | 3 | 2% |
Malaysia | 1 | <1% |
Netherlands | 1 | <1% |
Hungary | 1 | <1% |
Belgium | 1 | <1% |
Italy | 1 | <1% |
Russia | 1 | <1% |
Poland | 1 | <1% |
Other | 0 | 0% |
Unknown | 144 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 46 | 29% |
Student > Master | 25 | 16% |
Researcher | 16 | 10% |
Student > Bachelor | 14 | 9% |
Student > Doctoral Student | 13 | 8% |
Other | 21 | 13% |
Unknown | 23 | 15% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 51 | 32% |
Computer Science | 36 | 23% |
Agricultural and Biological Sciences | 13 | 8% |
Neuroscience | 10 | 6% |
Psychology | 8 | 5% |
Other | 12 | 8% |
Unknown | 28 | 18% |