Structural Feature Selection in Common Spatial Patterns Using Adaptive Sparse Group Lasso

ABSTRACT With the advancement of brain–computer interfaces (BCI), motor imagery (MI) electroencephalogram (EEG) decoding can greatly benefit from spatial filtering features derived from common spatial patterns (CSP). However, CSP‐based features often exhibit high redundancy and intersubject variability. These limitations make the feature selection methods based on sparse learning difficult to effectively balance the heterogeneous contributions of different temporal and spatial components. Moreover, these models tend to prioritise features with larger coefficients, potentially overlooking intrinsic feature importance and compromising the quality of the selected feature subset. To address these issues, we propose an Adaptive Sparse Group Lasso (ASGL) method for structured feature selection, designed to enhance discriminative CSP features whilst suppressing irrelevant components. The proposed method partitions EEG signals into consecutive segments using a sliding window, treating each as a separate feature group. Benefiting from this, the importance of features at both the group level and the within‐group level can be effectively quantified through mutual information and copula mutual information, thereby assigning adaptive weights for selective penalisation within the model. This weight construction strategy preserves important features from relevant time intervals and frequency bands. The resulting optimization problem is solved efficiently via the alternating direction method of multipliers (ADMM). Evaluations on simulated and real‐world datasets demonstrate that the proposed ASGL outperforms existing methods.