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

Nonstationary learning 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 nonstationary 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 nonstationary adaptation in MI-related BCI system based on CS detection applied to the temporal and spatial filtered features extracted from raw EEG signals. The chapter collectively provides an efficient method for accounting nonstationarity in EEG data during learning in NSEs

Current Source Density Estimation Enhances the Performance of Motor-Imagery Related Brain-Computer Interface

The objective is to evaluate the impact of EEG referencing schemes and spherical surface Laplacian (SSL) methods on the classification performance of motor-imagery (MI)-related brain-computer interface systems. Two EEG referencing schemes: common referencing and common average referencing and three surface Laplacian methods: current source density (CSD), finite difference method, and SSL using realistic head model were implemented separately for pre-processing of the EEG signals recorded at the scalp. A combination of filter bank common spatial filter for features extraction and support vector machine for classification was used for both pairwise binary classifications and four-class classification of MI tasks. The study provides three major outcomes: 1) the CSD method performs better than CR, providing a significant improvement of 3.02% and 5.59% across six binary classification tasks and four-class classification task, respectively; 2) the combination of a greater number of channels at the pre-processing stage as compared with the feature extraction stage yields better classification accuracies for all the Laplacian methods; and 3) the efficiency of all the surface Laplacian methods reduced significantly in the case of a fewer number of channels considered during the pre-processing