Results 81 to 90 of about 44,498 (172)

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

Information
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 ...
Pablo Andres Muñoz-Gutierrez, Diego Fernando Ramirez-Jimenez, Eduardo Giraldo   +2 more
doaj   +1 more source

Feature Extraction and Simulation of EEG Signals During Exercise-Induced Fatigue

IEEE Access, 2019
Accurate extraction of EEG signal characteristics during exercise fatigue can provide a scientific basis for sports fatigue detection and exercise fatigue injury treatment.
Zhongwan Yang, Huijie Ren
doaj   +1 more source

AI inspired EEG-based spatial feature selection method using multivariate empirical mode decomposition for emotion classification. [PDF]

Multimed Syst, 2022
Asghar MA, Khan MJ, Rizwan M, Shorfuzzaman M, Mehmood RM.   +4 more
europepmc   +1 more source

Frequency-Resolved Dynamic Functional Connectivity Reveals Scale-Stable Features of Connectivity-States

Frontiers in Human Neuroscience, 2018
Investigating temporal variability of functional connectivity is an emerging field in connectomics. Entering dynamic functional connectivity by applying sliding window techniques on resting-state fMRI (rs-fMRI) time courses emerged from this topic.
Markus Goldhacker, Markus Goldhacker, Ana M. Tomé, Ana M. Tomé, Mark W. Greenlee, Elmar W. Lang, Elmar W. Lang   +6 more
doaj   +1 more source

Exploring Scale-Specific Controls on Soil Water Content across a 500-Kilometer Transect Using Multivariate Empirical Mode Decomposition

Vadose Zone Journal, 2018
Soil water content (SWC) varies both spatially and temporally and is highly controlled by various factors operating at different intensities and scales.
Yali Zhao, Yunqiang Wang, Xingchang Zhang, Li Wang   +3 more
doaj   +1 more source

Advances and applications of empirical mode decomposition and its variants in hydrology: A review

Guan'gai paishui xuebao
Hydrological series are influenced by climate change, ecological succession, and human activities, containing complex, multi-layered, and interactive information that reflects highly non-linear and non-stationary characteristics.
CHEN Yunfei, LIU Zuyu, LIU Xiuhua, HE Junqi, ZHENG Ce, MA Yandong   +5 more
doaj   +1 more source

Score Function Features for Discriminative Learning: Matrix and Tensor Framework [PDF]

, 2014
Feature learning forms the cornerstone for tackling challenging learning problems in domains such as speech, computer vision and natural language processing.
Anandkumar, Anima, Janzamin, Majid, Sedghi, Hanie   +2 more
core   +1 more source

Chaotic signals denoising using empirical mode decomposition inspired by multivariate denoising

International Journal of Electrical and Computer Engineering (IJECE), 2020
Empirical mode decomposition (EMD) is an effective noise reduction method to enhance the noisy chaotic signal over additive noise. In this paper, the intrinsic mode functions (IMFs) generated by EMD are thresholded using multivariate denoising. Multivariate denoising is multivariable denosing algorithm that is combined wavelet transform and principal ...
openaire   +2 more sources

Efficient GPU implementation of the multivariate empirical mode decomposition algorithm

Journal of Computational Science, 2023
Zeyu Wang, Zoltan Juhasz
openaire   +1 more source

Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness [PDF]

Given the hard problem of consciousness (Chalmers, 1995) there are no brain electrophysiological correlates of the subjective experience (the felt quality of redness or the redness of red, the experience of dark and light, the quality of depth in a ...
Pereira, Vitor Manuel Dinis
core