Removal of electrical stimulus artifact in local field potential recorded from subthalamic nucleus by using manifold denoising

Tzu Chi Liu, Yi Chieh Chen, Po Lin Chen, Po Hsun Tu, Chih Hua Yeh, Mun Chun Yeap, Yi Hui Wu, Chiung Chu Chen, Hau Tieng Wu

Research output: Contribution to journalArticlepeer-review


Background: Deep brain stimulation (DBS) is an effective treatment for movement disorders such as Parkinson's disease (PD). However, local field potentials (LFPs) recorded through lead externalization during high-frequency stimulation (HFS) are contaminated by stimulus artifacts, which require to be removed before further analysis. New Method: In this study, a novel stimulus artifact removal algorithm based on manifold denoising, termed Shrinkage and Manifold-based Artifact Removal using Template Adaptation (SMARTA), was proposed to remove artifacts by deriving a template for each stimulus artifact and subtracting it from the signal. Under a low-dimensional manifold assumption, a matrix denoising technique called optimal shrinkage was applied to design a similarity metric such that the template for stimulus artifacts could be accurately recovered. Result: SMARTA was evaluated using semirealistic signals, which were the combination of semirealistic stimulus artifacts recorded in an agar brain model and LFPs of PD patients with no stimulation, and realistic LFP signals recorded in patients with PD during HFS. The results indicated that SMARTA removes stimulus artifacts with a modest distortion in LFP estimates. Comparison with Existing Methods: SMARTA was compared with moving-average subtraction, sample-and-interpolate technique, and Hampel filtering. Conclusion: The proposed SMARTA algorithm helps the exploration of the neurophysiological mechanisms of DBS effects.

Original languageEnglish (US)
Article number110038
JournalJournal of Neuroscience Methods
StatePublished - Mar 2024


  • Deep brain stimulation
  • Manifold denoising
  • Optimal shrinkage
  • Stimulus artifact removal

ASJC Scopus subject areas

  • General Neuroscience


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