Semi-Supervised Neural Gas for Adaptive Brain-Computer Interfaces

Riechmann H, Finke A. Semi-Supervised Neural Gas for Adaptive Brain-Computer Interfaces. In: ESANN 2012 proceedings. i6doc.com; 2012: 121-126.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