TY  - BOOK
AB  - 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.
DA  - 2012
KW  - Neural Gas
KW  - Semi-Supervised Learning
KW  - Adaptive
KW  - EEG
KW  - Motor Imagery
KW  - Fuzzy
KW  - Brain-Computer Interface
LA  - eng
PY  - 2012
TI  - Semi-Supervised Neural Gas for Adaptive Brain-Computer Interfaces
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-24649894
Y2  - 2024-11-22T03:00:09
ER  -