EEG non-stationarity across multiple sessions during a Motor Imagery-BCI intervention: two post stroke case series

Clinical Electroencephalogram (EEG) Brain-Computer-Interface (BCI) rehabilitation largely depend on reliable information extraction from steadily evolving brain features. Non-stationary EEG feature behavior is considered a major challenge and a lot of effort has been devoted to developing adaptive methods to accommodate for this nonstationarity. However, learning- and plasticity-related mechanisms throughout a BCI intervention are additional sources of non-stationarity, that even though expected, we know very little about. In this work, we explore the evolution of Motor Imagery (MI) information extraction across multiple sessions, in two stroke patients, using a fixed and an adaptive Support Vector Machine (SVM) model. We show different behavior of the fixed SVM model for the two patients, indicating that for one patient, relevant MI-related EEG features shifted throughout the intervention. This observation calls for further investigations to better understand the evolution and shift of features across sessions, as well as the impact of using adaptive methods from a clinical outcome perspective.