Open AccessAutomatic decomposition of electrophysiological data into distinct nonsinusoidal oscillatory modes
Author(s) -
Marco S. Fabus,
Andrew Quinn,
Catherine E. Warnaby,
Mark W. Woolrich
Publication year - 2021
Publication title -
journal of neurophysiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.302
H-Index - 245
eISSN - 1522-1598
pISSN - 0022-3077
DOI - 10.1152/jn.00315.2021
Subject(s) - computer science , hilbert–huang transform , electrophysiology , mixing (physics) , decomposition , biological system , pattern recognition (psychology) , artificial intelligence , neuroscience , physics , chemistry , biology , organic chemistry , filter (signal processing) , quantum mechanics , computer vision
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.