Noise-Assisted Multivariate Empirical Mode Decomposition Based Emotion Recognition
Electrica, 2018 Emotion state detection or emotion recognition cuts across different disciplines because of the many parameters that embrace the brain's complex neural structure, signal processing methods, and pattern recognition algorithms.
Pınar Özel +2 more
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Fast Multivariate Empirical Mode Decomposition
IEEE Access, 2018 The multivariate empirical mode decomposition (MEMD) has been pioneered recently for adaptively processing of multichannel data. Despite its high efficiency on time-frequency analysis of nonlinear and nonstationary signals, high computational load and ...
Xun Lang +6 more
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Dynamically sampled multivariate empirical mode decomposition [PDF]
Electronics Letters, 2015 A method for accurate multivariate local mean estimation in the multivariate empirical mode decomposition algorithm by using a statistical data‐driven approach based on the Menger curvature measure and normal‐to‐anything variate‐generation method is proposed. This is achieved by aligning the projection vectors in the direction of the maximum ‘activity’
Rehman, N +4 more
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Measuring Center of Pressure Signals to Quantify Human Balance Using Multivariate Multiscale Entropy by Designing a Force Platform [PDF]
Sensors, 2013 To assess the improvement of human body balance, a low cost and portable measuring device of center of pressure (COP), known as center of pressure and complexity monitoring system (CPCMS), has been developed for data logging and analysis.
Cheng-Wei Huang +4 more
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Seizure detection from EEG signals using Multivariate Empirical Mode Decomposition [PDF]
Computers in Biology and Medicine, 2017 We present a data driven approach to classify ictal (epileptic seizure) and non-ictal EEG signals using the multivariate empirical mode decomposition (MEMD) algorithm. MEMD is a multivariate extension of empirical mode decomposition (EMD), which is an established method to perform the decomposition and time-frequency (T-F) analysis of non-stationary ...
Asmat Zahra +4 more
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Bidimensional Multivariate Empirical Mode Decomposition With Applications in Multi-Scale Image Fusion [PDF]
IEEE Access, 2019 Empirical mode decomposition (EMD) is a fully data-driven technique designed for multi-scale decomposition of signals into their natural scale components, called intrinsic mode functions (IMFs).
Yili Xia +3 more
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Weighted Window Sliding Multivariate Empirical Mode Decomposition for Online Multichannel Filtering
IEEE Access, 2018 Affected by nonlinear and non-stationary problems, classical linear analysis approaches may fail in analyzing real-world signals, such as the biomedical data.
Songbing Tao +3 more
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GPU-Accelerated Multivariate Empirical Mode Decomposition for Massive Neural Data Processing
IEEE Access, 2017 This paper presents an efficient implementation of multivariate empirical mode decomposition (MEMD) algorithm, a multivariate extension of EMD algorithm.
Taha Mujahid +2 more
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Deep Multi-Stage Reference Evapotranspiration Forecasting Model: Multivariate Empirical Mode Decomposition Integrated With the Boruta-Random Forest Algorithm [PDF]
IEEE Access, 2021 Evapotranspiration, as a combination of evaporation and transpiration of water vapour, is a primary component of global hydrological cycles. It accounts for significant loss of soil moisture from the earth to the atmosphere.
W. J. M. Lakmini Prarthana Jayasinghe +4 more
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Multivariate Nonstationary Oscillation Simulation of Climate Indices With Empirical Mode Decomposition [PDF]
Water Resources Research, 2019 The objective of the current study is to build a stochastic model to simulate climate indices that are teleconnected with the hydrologic regimes of large‐scale water resources systems such as the Great Lakes system.
Taesam Lee, Taha B.M.J. Ouarda
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