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

TL;DR

This paper introduces SANAR, an unsupervised manifold learning algorithm that effectively removes electrical stimulation artifacts from intracranial EEG data, enabling better analysis of neurophysiological signals during cortical stimulation.

Contribution

SANAR is a novel artifact removal method that overcomes nonstationarity challenges, outperforming ICA in preserving endogenous signals during stimulation.

Findings

SANAR effectively removes artifacts in simulated and real iEEG data.

It preserves spectral content better than ICA.

Performance exceeds existing methods in retaining endogenous activity.

Abstract

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. 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.