Five-class motor imagery BCI classification and its application to brain-controlled wheelchairs

Brain-controlled wheelchair (BCW) technology enables direct wheelchair control via brain-computer interfaces (BCIs), eliminating the need for physical limb interaction. Motor imagery-based BCIs (MI-BCIs) are widely used in non-invasive BCIs due to their ability to provide intuitive neural control without external stimuli. However, developing a BCW system based on MI-BCIs remains challenging, particularly in achieving reliable multi-class classification accuracy.To address this challenge, this study proposes an advanced feature extraction algorithm to enhance MI-BCI performance using a custom-built five-class MI-EEG dataset. The proposed method, EHT-CSP, integrates Ensemble Empirical Mode Decomposition Hilbert-Huang Transform (EEMD-HHT) with Time-Frequency Common Spatial Pattern (TFCSP). Specifically, it extracts marginal spectrum entropy and energy spectrum entropy via EEMD-HHT. It then combines these features with TFCSP-derived feature vectors to improve feature discrimination. The Light Gradient Boosting Machine is then employed for classification. The proposed MI-BCI system is evaluated through both offline analysis and real-world BCW obstacle avoidance experiments. Results demonstrate that the algorithm achieves an average classification accuracy of 78.45%, with all participants successfully completing BCW navigation tasks. In this study, LightGBM and EHT-CSP are compared with other algorithms respectively, and it is verified that the proposed model is superior to the existing models.