Abstract

Adaptive deep brain stimulation (aDBS) modulates neural activity based on symptom-related biomarkers, promising improved efficacy and energy efficiency over conventional DBS . A persistent challenge, however, is stimulation-induced artifacts that corrupt neural recordings and compromise real-time biomarker extraction. We present SMARTA , a computationally efficient algorithm for removing both periodic stimulation and transient DC artifacts. SMARTA leverages modern random matrix theory to model local field potentials as noise with a separable covariance structure, and employs an approximate nearest neighbor scheme to achieve real-time performance. Using semi-real aDBS and Parkinson’s patient data, SMARTA outperforms existing methods in artifact suppression, preserves spectral–temporal features from beta to high-frequency oscillations, and improves beta-burst detection. These results highlight SMARTA as a mathematically principled and practical tool for advancing closed-loop neuromodulation.