Semi-Supervised Neural Gas for Adaptive Brain-Computer Interfaces

Riechmann H, Finke A (2012)
In: ESANN 2012 proceedings. i6doc.com: 121-126.

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Conference Paper | Published | English
Abstract
Non-stationarity is inherent in EEG data. We propose a concept for an adaptive brain computer interface (BCI) that adapts a classifier to the changes in EEG data. It combines labeled and unlabeled data acquired during normal operation of the system. The classifier is based on Fuzzy Neural Gas (FNG), a prototype-based classifier. Based on four data sets we show that retraining the classifier significantly increases classification accuracy. Our approach smoothly adapts to the session-to-session variations in the data.
Publishing Year
Conference
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Location
Brugge, Belgium
Conference Date
2012-04-25
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Riechmann H, Finke A. Semi-Supervised Neural Gas for Adaptive Brain-Computer Interfaces. In: ESANN 2012 proceedings. i6doc.com; 2012: 121-126.
Riechmann, H., & Finke, A. (2012). Semi-Supervised Neural Gas for Adaptive Brain-Computer Interfaces. ESANN 2012 proceedings, 121-126.
Riechmann, H., and Finke, A. (2012). “Semi-Supervised Neural Gas for Adaptive Brain-Computer Interfaces” in ESANN 2012 proceedings (i6doc.com), 121-126.
Riechmann, H., & Finke, A., 2012. Semi-Supervised Neural Gas for Adaptive Brain-Computer Interfaces. In ESANN 2012 proceedings. i6doc.com, pp. 121-126.
H. Riechmann and A. Finke, “Semi-Supervised Neural Gas for Adaptive Brain-Computer Interfaces”, ESANN 2012 proceedings, i6doc.com, 2012, pp.121-126.
Riechmann, H., Finke, A.: Semi-Supervised Neural Gas for Adaptive Brain-Computer Interfaces. ESANN 2012 proceedings. p. 121-126. i6doc.com (2012).
Riechmann, Hannes, and Finke, Andrea. “Semi-Supervised Neural Gas for Adaptive Brain-Computer Interfaces”. ESANN 2012 proceedings. i6doc.com, 2012. 121-126.
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2012-08-01 12:32:55

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