Learning with Covariate Shift-Detection and Adaptation in Non-Stationary Environments : Application to Brain-Computer Interface

Learning in the presence of dataset shifts in non-stationary environments is a major challenge. Dataset shifts in the form of covariate shifts commonly occur in a broad range of real-world systems such as, electroencephalogram (EEG) based brain-computer interfaces (BCIs). Under covariate shifts, the properties of the input data distribution may shift over time from training to test/operating phase. In such systems, there is a need for continuous monitoring of the process behavior and tracking the state of the shifts to decide about initiating adaptation in a timely manner. This paper presents a covariate shift-detection and adaptation methodology, and its application to motor-imagery based BCIs. An exponential weighted moving average (EWMA) model based test is used for the covariate shift-detection in the features of EEG signals. The proposed algorithm initiates the adaptation by reconfiguring the knowledge-base of the classifier. Its performance is evaluated through experiments using a real-world dataset i.e. BCI Competition IV dataset 2A. Results show that the proposed methodology effectively performs covariate-shift-detection and adaptation and it can help to realize adaptive BCI systems.