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Diffusion geometry approach to efficiently remove electrical stimulation artifacts in intracranial

Sankaraleengam Alagapan^1,2, Hae Won Shin^3,4, Flavio Fröhlich^1,2,3,5,6,7

¹Carolina Center for Neurostimulation, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America.

Journal of Neural Engineering

|December 8, 2018

Summary

A new algorithm, SANAR, effectively removes electrical stimulation artifacts from intracranial electroencephalography (iEEG) data. This method preserves neural signal integrity, enabling better analysis of brain activity during direct cortical stimulation (DCS) for therapeutic development.

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Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Cortical oscillations are crucial for cognitive functions but are impaired in psychiatric disorders.
Intracranial electroencephalography (iEEG) observes these oscillations, and direct cortical stimulation (DCS) offers therapeutic potential.
Electrical stimulation artifacts in iEEG data hinder the analysis of DCS effects, especially with non-stationary biological data.

Purpose of the Study:

To develop a novel algorithm for removing electrical stimulation artifacts from iEEG data.
To overcome limitations of existing artifact removal methods in the presence of biological data nonstationarity.
To enable accurate analysis of iEEG data during DCS for advancing therapeutic strategies.

Main Methods:

Developed the Shape Adaptive Nonlocal Artifact Removal (SANAR) algorithm based on unsupervised manifold learning.
Employed Euclidean median estimation of k-nearest neighbors in a nonlocal manner to represent and subtract artifacts.
Validated the algorithm on simulated and human iEEG datasets.

Main Results:

SANAR effectively removes stimulation artifacts in the time domain while preserving the spectral content of endogenous neurophysiological signals.
The algorithm's performance surpasses independent component analysis (ICA) in both time and frequency domains.
Quantitative measures confirm the retention of endogenous activity information by SANAR.

Conclusions:

SANAR provides a robust solution for artifact removal in iEEG data acquired during DCS.
This method facilitates the analysis of neural activity during stimulation, crucial for understanding its effects.
The approach supports the development of new, targeted therapies for neurological and psychiatric disorders.