| Title | Semi-Supervised Neural Gas for Adaptive Brain-Computer Interfaces |
| Publication Type | Conference Paper |
| Year of Publication | In Press |
| Authors | Riechmann, Hannes, and Finke Andrea |
| Refereed Designation | Does Not Apply |
| Conference Name | European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
| Conference Start Date | 25/04/2012 |
| Conference Location | Brugge, Belgium |
| 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. |
