Graph-based Recurrence Quantification Analysis of EEG Spectral Dynamics for Motor Imagery-based BCIs

Abstract

Despite continuous research, communication approaches based on brain-computer interfaces (BCIs) are not yet an efficient and reliable means that severely disabled patients can rely on. To date, most motor imagery (MI)-based BCI systems use conventional spectral analysis methods to extract discriminative features and classify the associated electroencephalogram (EEG)-based sensorimotor rhythms (SMR) dynamics that results in relatively low performance. In this study, we investigated the feasibility of using recurrence quantification analysis (RQA) and complex network theory graph-based feature extraction methods as a novel way to improve MI-BCIs performance. Rooted in chaos theory, these features explore the nonlinear dynamics underlying the MI neural responses as a new informative dimension in classifying MI.

Method: EEG time series recorded from six healthy participants performing MI-Rest tasks were projected into multidimensional phase space trajectories in order to construct the corresponding recurrence plots (RPs). Eight nonlinear graph-based RQA features were extracted from the RPs then compared to the classical spectral features through a 5-fold nested cross-validation procedure for parameter optimization using a linear support vector machine (SVM) classifier.

Results: Nonlinear graph-based RQA features were able to improve the average performance of MI-BCI by 5.8% as compared to the classical features.

Significance: These findings suggest that RQA and complex network analysis could represent new informative dimensions for nonlinear characteristics of EEG signals in order to enhance the MI-BCI performance.