Covariate shift detection-based nonstationary adaptation in motor-imagery-based brain–computer interface

Abstract

Non-stationary learning (NSL) refers to the process that can learn patterns from data, adapt to shifts, and improve performance of the system with its experience while operating in the non-stationary environments (NSEs). Covariate shift (CS) presents a major challenge during data processing within NSEs wherein the input-data distribution shifts during transitioning from training to testing phase. CS is one of the fundamental issues in electroencephalogram (EEG) based brain-computer interface (BCI) systems and can be often observed during multiple trials of EEG data recorded over different sessions. Thus, conventional learning algorithms struggle to accommodate these CSs in streaming EEG data resulting in low performance (in terms of classification accuracy) of motor imagery (MI)-related BCI systems. This chapter aims to introduce a novel framework for non-stationary adaptation in MI-related BCI system based on covariate shift detection (CSD) applied to the temporal and spatial filtered features extracted from raw EEG signals. The chapter collectively provides an efficient method for accounting non-stationarity in EEG data during learning in NSEs.