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- W8657122 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." @default.
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- W8657122 date "2012-01-01" @default.
- W8657122 modified "2023-09-28" @default.
- W8657122 title "Semi-Supervised Neural Gas for Adaptive Brain-Computer Interfaces" @default.
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