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Multi-domain feature extraction and Sand Cat Swarm Optimized Broad Learning System for EEG-based Motor Imagery

Vaishali R Shirodkar1, Damodar Reddy Edla2, Annu Kumari3

  • 1Department of Computer Science and Engineering, National Institute of Technology Goa, Kottamoll Plateau, Cuncolim, 403703, Goa, India; Information Technology Department, Goa College of Engineering, Farmagudi, Ponda, 403401, Goa, India.

Computers in Biology and Medicine
|November 9, 2025
PubMed
Summary

This study introduces an efficient Brain-Computer Interface (BCI) system using Electroencephalography (EEG) for intuitive control. The novel architecture achieves high accuracy in classifying brain activity for motor imagery tasks, even with individual differences.

Keywords:
Brain–Computer InterfaceBroad Learning SystemHilbert Huang TransformMotor ImageryRiemannian GeometrySand Cat Swarm Optimization

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Brain-Computer Interfaces (BCIs) offer non-invasive control via brain activity, with Motor Imagery (MI) systems being prominent.
  • Electroencephalography (EEG) is favored for its portability and temporal resolution, but its non-stationary and subject-specific signals present classification challenges.
  • Existing BCI systems struggle with reliable classification due to the inherent variability and complexity of EEG data.

Purpose of the Study:

  • To develop a lightweight and efficient classification architecture for MI-based BCIs.
  • To enhance the accuracy and generalization of EEG signal classification.
  • To address the challenges of non-stationarity and inter-subject variability in EEG data.

Main Methods:

  • A novel architecture combining Event-Related Desynchronization (ERD) band selection, Empirical Mode Decomposition (EMD), Hilbert-Huang Transform (HHT), Riemannian Geometry (RG), and Common Spatial Pattern (CSP) feature extraction.
  • Integration of a Broad Learning System (BLS) classifier with parameters optimized by the Sand Cat Swarm Optimization (SCSO) algorithm.
  • Utilized two datasets (BCI IV 2a and a clinical stroke EEG dataset) for performance evaluation, including All-subjects and Leave-One-Subject-Out (LOSO) validation.

Main Results:

  • Achieved high classification accuracies: 90.78% on the BCI IV 2a dataset and 96.41% on the clinical stroke EEG dataset.
  • Demonstrated competitive generalization performance across different validation settings, indicating robustness to inter-subject variability.
  • The proposed pipeline effectively extracts discriminative features and handles individual differences in EEG signals.

Conclusions:

  • The proposed lightweight and efficient BCI classification architecture significantly improves accuracy and generalization.
  • The integration of EMD, HHT, RG, and CSP with an SCSO-optimized BLS classifier effectively models nonlinear EEG dynamics and addresses inter-subject variability.
  • This approach shows strong potential for real-world BCI applications, particularly in motor imagery tasks and clinical settings like stroke rehabilitation.