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

Abstract

Electroencephalogram (EEG)-based motor imagery (MI) (MI-EEG) 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 multiclass 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 hyperparameters in swarm decomposition, and further applied to enhance MI-EEG classification. ESSDM adopts improved swarm filtering to automatically deliver optimal frequency bands in the sparse spectrum with optimized hyperparameters to extract dominant oscillatory components (OCs) that significantly enhance MI activation-related sub-bands. In addition, new fitness criteria has been designed based on the Kullback–Leibler divergence distance from the spectral kurtosis of the obtained modes to select hyperparameters that optimize decomposition effect, avoid excessive iterations, and provide fast convergence with optimal modes. Further, fused time-frequency graph (FTFG) features have been derived from computed time-frequency representation to find cross-channel mutual spectral information. The experimental results on the 2-class BCI III-4a, 2-class OpenBMI, and 4-class BCI IV-2a datasets reveal that the proposed framework based on FTFG features with capsule neural network (CapsNet), ESSDM-FTFG-CapsNet outperformed other existing methods in specific-subject, cross-subject, and cross-session scenarios. © 2004-2012 IEEE.