GPU-Accelerated Multivariate Empirical Mode Decomposition for Massive Neural Data Processing

GPU-Accelerated Multivariate Empirical Mode Decomposition for Massive Neural Data Processing

This paper presents an efficient implementation of multivariate empirical mode decomposition (MEMD) algorithm, a multivariate extension of EMD algorithm. Analogous to EMD, MEMD decomposes a multivariate signal into its intrinsic mode functions using joint rotational mode. The algorithm is computatio...

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Bibliographic Details
Main Authors: Taha Mujahid, Anis Ur Rahman, Muhammad Murtaza Khan
Format: Article
Language:English
Published: IEEE 2017-01-01
Series:IEEE Access
Subjects:
GPGPU
multivariate empirical mode decomposition
neural processing
Online Access:https://ieeexplore.ieee.org/document/7932860/

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