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We introduce a novel, data-driven method to identify oscillations in neural recordings. This approach is based on empirical mode decomposition and reduces mixing of components, one of its main problems. The technique is validated and compared with existing methods using simulations and real data. We show our method better extracts oscillations and their properties in highly noisy and nonsinusoidal datasets.

Original publication

DOI

10.1152/jn.00315.2021

Type

Journal article

Journal

Journal of Neurophysiology

Publisher

American Physiological Society

Publication Date

01/11/2021

Volume

126

Pages

1670 - 1684