English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Cortical oscillations, electrophysiological activity patterns, associatedwith cognitive functions and impaired in many psychiatric disorders can beobserved in intracranial electroencephalography (iEEG). Direct corticalstimulation (DCS) may directly target these oscillations and may serve astherapeutic approaches to restore functional impairments. However, the presenceof electrical stimulation artifacts in neurophysiological data limits theanalysis of the effects of stimulation. Currently available methods suffer inperformance in the presence of nonstationarity inherent in biological data. Ouralgorithm, Shape Adaptive Nonlocal Artifact Removal (SANAR) is based onunsupervised manifold learning. By estimating the Euclidean median of k nearestneighbors of each artifact in a nonlocal fashion, we obtain a faithfulrepresentation of the artifact which is then subtracted. This approachovercomes the challenges presented by nonstationarity. SANAR is effective inremoving stimulation artifacts in the time domain while preserving the spectralcontent of the endogenous neurophysiological signal. We demonstrate theperformance in a simulated dataset as well as in human iEEG data. Using twoquantitative measures, that capture how much of information from endogenousactivity is retained, we demonstrate that SANAR's performance exceeds that ofone of the widely used approaches, independent component analysis, in the timedomain as well as the frequency domain. This approach allows for the analysisof iEEG data, single channel or multiple channels, during DCS, a crucial stepin advancing our understanding of the effects of periodic stimulation anddeveloping new therapies.

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