Transfer learning for EEG based BCI using LEARN++.NSE and mutual information

In this paper, the use of mutual information and the Learn++.NSE algorithm is proposed to create an EEG SSVEP BCI system that can select and utilize data sets originating from a group of users. In typical BCI systems, the nonstationarity in the EEG prevents the system from blindly applying training data from other users to the incoming data. Mutual information is introduced to select previous data sets that provide the most information about current random variables. A signed rank test was employed to show that this configuration outperformed both normal Learn++.NSE ensembles and LDA classifiers. This indicates that mutual information and ensemble learning techniques may prove useful in improving user transferability in SSVEP systems with low computational requirements.