Transferring Unsupervised Adaptive Classifiers Between Users Of A Spatial Auditory Brain-Computer Interface
Pieter-Jan Kindermans, Benjamin Schrauwen, Benjamin Blankertz, Mller, Klaus-Robert, Michael Tangermann · Ghent University Academic Bibliography (Ghent University) · 2020
The transfer of knowledge allows to rapidly set up a functioning BCI system for a novel user without user specific calibration.For spelling paradigms based on event-related potentials (ERP) of the EEG, a recent unsupervised classification method is able to train the classifier online by constantly adapting to the user on the fly.Hence, an explicit calibration phase can be avoided.However, due to the random initialization of the classifier, this method requires a warm-up time before it performs on the same level as a supervised trained classifier.This warm-up effect can be reduced by transfer learning.We present a thorough leave-one-user-out offline analysis (n=9 users) and additional preliminary online results from a spatial auditory ERP spelling study (AMUSE paradigm) on inter-subject transfer of an unsupervised adaptive classifier.For the online study, a classifier trained on data of n=8 previous users was transferred to two unseen users and further adapted online.The performance was evaluated in one online copy spelling session per user.Both, the offline simulations and the online results indicate that the transfer approach reduces the warm-up time by approx.50 %.