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Deep LSTM Surrogates for MEMD: A Noise-Assisted Approach to EEG Intrinsic Mode Function Extraction

In this paper, we propose a deep learning-based surrogate model for Multivariate Empirical Mode Decomposition (MEMD) using Long Short-Term Memory (LSTM) networks, aimed at efficiently extracting Intrinsic Mode Functions (IMFs) from electroencephalographic (EEG) signals. Unlike traditional data-drive...

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Principais autores: Pablo Andres Muñoz-Gutierrez, Diego Fernando Ramirez-Jimenez, Eduardo Giraldo
Format: Artigo
Sprog:Inglês
Udgivet: MDPI AG 2025-08-01
Serier:Information
Fag:
multivariate empirical mode decomposition
long short-term memory
surrogate
electroencephalography
Online adgang:https://www.mdpi.com/2078-2489/16/9/754
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