Diffusion geometry approach to efficiently remove electrical stimulation artifacts in intracranial electroencephalography.

Journal Article (Journal Article)


Cortical oscillations, electrophysiological activity patterns, associated with cognitive functions and impaired in many psychiatric disorders can be observed in intracranial electroencephalography (iEEG). Direct cortical stimulation (DCS) may directly target these oscillations and may serve as therapeutic approaches to restore functional impairments. However, the presence of electrical stimulation artifacts in neurophysiological data limits the analysis of the effects of stimulation. Currently available methods suffer in performance in the presence of nonstationarity inherent in biological data.


Our algorithm, shape adaptive nonlocal artifact removal (SANAR) is based on unsupervised manifold learning. By estimating the Euclidean median of k-nearest neighbors of each artifact in a nonlocal fashion, we obtain a faithful representation of the artifact which is then subtracted. This approach overcomes the challenges presented by nonstationarity.

Main results

SANAR is effective in removing stimulation artifacts in the time domain while preserving the spectral content of the endogenous neurophysiological signal. We demonstrate the performance in a simulated dataset as well as in human iEEG data. Using two quantitative measures, that capture how much of information from endogenous activity is retained, we demonstrate that SANAR's performance exceeds that of one of the widely used approaches, independent component analysis, in the time domain as well as the frequency domain.


This approach allows for the analysis of iEEG data, single channel or multiple channels, during DCS, a crucial step in advancing our understanding of the effects of periodic stimulation and developing new therapies.

Full Text

Duke Authors

Cited Authors

  • Alagapan, S; Shin, HW; Fröhlich, F; Wu, H-T

Published Date

  • June 2019

Published In

Volume / Issue

  • 16 / 3

Start / End Page

  • 036010 -

PubMed ID

  • 30523899

Electronic International Standard Serial Number (EISSN)

  • 1741-2552

International Standard Serial Number (ISSN)

  • 1741-2560

Digital Object Identifier (DOI)

  • 10.1088/1741-2552/aaf2ba


  • eng