Electroencephalography (EEG)-based motor imagery (MI) decoding has
established a novel experimental paradigm in brain-computer interface
(BCI) applications that offer effective treatment for stroke paralyzed
patients. However, existing MI-EEG-based BCI systems introduce
deployment issues because of nonstationary EEG signals, suboptimal
features, and limited multi-class scalability. To tackle these issues,
we propose an enhanced sparse swarm decomposition method (ESSDM) based
on selfish-herd optimization and sparse spectrum to solve the issue of
choice of uniform decomposition and hyper-parameters in swarm
decomposition and applied to enhance MI-EEG classification. ESSDM adopts
improved swarm filtering to automatically deliver optimal frequency
bands in sparse spectrums with optimized hyper-parameters to extract
dominant oscillatory components (OCs) that significantly enhance MI
activation-related sub-bands. In addition, new fitness criteria is
designed based on the Kullback–Leibler divergence distance from
spectral kurtosis of obtained modes to select hyper-parameters that
optimize decomposition effect, avoid excessive iterations, and provide
fast convergence with optimal modes. Further, fused time-frequency graph
(FTFG) features were derived from computed time-frequency representation
to find cross-channel mutual spectral information. The experimental
results on the 2-class BCI III-4a and 4-class BCI IV-2a datasets reveal
that the proposed FTFG feature with CapsNet classifier framework
(ESSDM-FTFG-CapsNet) outperformed existing methods in specific-subject
and cross-subject scenarios.