ESSDM: An Enhanced Sparse Swarm Decomposition Method and Its Application in Multi‐class Motor Imagery–Based EEG-BCI System

Abstract

<p>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. </p>