An SVD-based method for DBS artifact removal: High-fidelity restoration of local field potential

Abstract

Background and Objective:

Deep brain stimulation (DBS) is widely used to treat neurological disorders. Recent advances have integrated DBS with local field potential (LFP) recordings to elucidate pathophysiological mechanisms and enhance therapeutic efficacy. However, the DBS pulse-induced artifacts severely contaminate LFP recordings and hinder accurate physiological signal retrieval and neural signal analysis. To solve this problem, we proposed an artifact removal method based on singular value decomposition (SVD) for effectively removing DBS-induced artifacts from LFP, enabling high-fidelity restoration of LFP signals during DBS procedure.

Methods:

The DBS-contaminated LFP signal undergoes detrending, and z-score normalization using the pre-DBS segment as baseline. Artifacts are detected via a z-threshold and further extended to include post-pulse direct current (DC) bias. The aligned segments are processed with SVD to extract and remove the artifact components, followed by linear interpolation for residual artifacts correction. The artifact-free segments are then reinserted into the original signal to produce an artifact-free signal output. Validation is conducted on both synthetic dataset and the real-world datasets from animal and human recordings.

Results:

Our method achieves over 98% signal restoration on synthetic datasets, outperforming three common artifact removal techniques while maintaining a comparable computational speed of $\sim$200 ms. It successfully restores LFP features and identifies key biomarkers in both animal and human DBS data.

Conclusion:

The proposed SVD-based method effectively removes DBS artifacts and restores physiological signals with high fidelity. It shows strong potential for identifying neural biomarkers essential for DBS and brain–computer interfaces (BCI), enhancing their precision and advancing the understanding of neural mechanisms in neurological disorders.