Unilateral Limb Motion Imagery Decoding Algorithm Based on Adaptive Band Boundary Localization

The unilateral limb motor imagery paradigm can effectively address the cognitive dissociation problem among multiple limbs and provide strong technical support for extending the functionality of external devices. However, feature mining and accurate decoding of unilateral limb movements remain challenging. In this study, we propose a feature mining method that combines automatic frequency band boundary localization with regularized common spatial pattern (AFBBL-RCSP), and employ a pinball-loss-based twin support vector machine (Pin-UTSVM) to decode EEG signals corresponding to reaching, turning, and grasping movements. First, multiple optimal frequency band boundaries were identified for each subject using AFBBL. Then, regularized spatial features were extracted from each sub-band, and all features were reduced using Fisher’s discriminant analysis. Finally, the Pin-UTSVM classifier was used to categorize the three types of movement data. The results show that, compared with CSP and RCSP feature mining methods using the fixed 8–30 Hz band, the proposed method improves decoding accuracy by 9.52% and 3.89%, respectively. Compared with fixed single-band feature mining methods based on the α band, β band, and α + β band, the proposed method improves accuracy by 5.56%, 3.89%, and 3.73%, respectively. In addition, compared with existing unilateral limb decoding methods based on temporal-spatial features, temporal-frequency features, and temporal-spatial-temporal-frequency fusion CNN features, the proposed method improves decoding accuracy by 34.93%, 34.09%, and 28.11%, respectively. These results suggest that the proposed AFBBL-RCSP method is effective for unilateral limb motor imagery EEG decoding.