To address the challenge of decoding motor imagery-based electroencephalography(EEG) signals, a casual sparse optimization method is proposed, focusing on selecting casual discriminative features to improve decoding performance. Specifically, the Hilbert-Huang transform(HHT) extracts marginal spectral energy from EEG channels. And the sample reweighted decorrelation operator(SRDO) optimizes features by weighting to eliminate spurious relationships among interfering features and discriminative features crucial for the decoding of motor imagery patterns. Particularly, the affinity propagation(AP) clustering algorithm identifies potential relationships among feature spatial distributions, and the iterative sparse-group lasso(iSGL) optimizes features by considering features' importance with intra- and inter-group to improve the decoding accuracy of motor imagery-based EEG signals. A 5-fold cross-validation experiment on a binary classification motor imagery dataset using the support vector machine(SVM) shows an average accuracy of 92.30%, nearly 4% higher than the original features. Comparative experiments demonstrate the superiority of the proposed method, suggesting its effectiveness for advancing brain-computer interfaces.