Covariate shift-adaptation using a transductive learning model for handling non-stationarity in EEG based brain-computer interfaces

A major challenge to devising robust brain-computer interfaces (BCIs) based on electroencephalogram (EEG) data is the immanent non-stationary characteristics of EEG signals. Statistical properties of the signals may shift during inter-or-intra session transfers that often leads to deteriorated BCI performance. The shift in the input data distribution from training to testing phase is called a covariate shift. It can be caused by various reasons such as different electrode placements, varying impedances and other ongoing brain activities. We propose an algorithm to handle this issue by adapting to the covariate shifts in the EEG data using a transductive learning approach. The performance of the proposed method is evaluated on the BCI competition 2008-Graz dataset B. The results show an improvement in classification accuracy of the BCI system over a traditional learning method. The obtained results support the conclusion that covariate-shift-adaptation using transductive learning is helpful to realize adaptive BCI systems.