A Framework for Online Inter-subjects Classification in Endogenous Brain-Computer Interfaces.

Inter-subjects classification and online adaptation techniques have been actively explored in the brain-computer interfaces (BCIs) research community during the last years. However, few works tried to conceive classification models that take advantage of both techniques. In this paper we propose an online inter-subjects classification framework for endogenous BCIs. Inter-subjects classification is performed using a weighted average ensemble in which base classifiers are learned using data recorded from different subjects and weighted according to their accuracies in classifying brain signals of current BCI user. Online adaptation is performed by updating base classifiers' weights in a semi-supervised way based on ensemble predictions reinforced by interaction error-related potentials (iErrPs). The effectiveness of our approach is demonstrated using two electroencephalography (EEG) data sets and a previously proposed procedure for simulating interaction error potentials.