Graph vertex and spectral features for EEG-based motor imagery classification

Motor imagery (MI) patterns play a vital role in brain-computer interface (BCI) systems, enabling control of external devices without relying on peripheral nerves or muscles. These patterns are typically classified by analyzing the associated electroencephalogram (EEG) signals. In this work, we introduce a novel MI classification approach based on multilevel graph-theoretic modeling of multichannel EEG signals. Multivariate autoregressive modeling and coherence analysis are firstly employed to construct directed graph signals to represent the relationships among EEG channels and capture the complex correlations inherent in MI patterns. Spatial graph vertex features are thus extracted as well as graph Fourier transform coefficients. Moreover, multilevel generalizations of vertex-domain features are thus defined where edges of graph signals are pruned according to different thresholds, vertex features are extracted for each threshold level, and then all features are combined into a multilevel hierarchical graph descriptor. These graph-theoretic descriptors could be fused with different variants of common spatial patterns for improved discriminability on MI classification tasks. Different feature combinations are used to train k-nearest neighbor classifiers, support vector machines, and random forests for MI pattern classification. The proposed method demonstrates competitive performance compared to the FWCSP and SCSP methods on Dataset 2a of the BCI Competition IV, as well as robust results on Dataset 1 from the same competition. Overall, the findings highlight the potential of multilevel spatial and spectral graph features in leveraging the correlation among EEG channels towards enhanced MI classification performance.