Abstract
Brain-Computer Interfaces (BCIs) can be used to give paralyzed patients a means for communication. But so far, only supervised methods have been used for calibration of an online BCI. In this paper we present a method that allows to calibrate a BCI online and unsupervised. Based on offline data we show that the unsupervised calibration method works and validate the results in an online experiment with 8 subjects, who were able to control the BCI with an average accuracy of 85 %. We thereby have shown for the first time that an online unsupervised calibration of a BCI is possible and allows for successful BCI control.
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Spüler, M., Rosenstiel, W., Bogdan, M. (2013). Unsupervised Online Calibration of a c-VEP Brain-Computer Interface (BCI). In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds) Artificial Neural Networks and Machine Learning – ICANN 2013. ICANN 2013. Lecture Notes in Computer Science, vol 8131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40728-4_28
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DOI: https://doi.org/10.1007/978-3-642-40728-4_28
Publisher Name: Springer, Berlin, Heidelberg
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