EEG gradient artifact removal by compressive sensing and Taylor-Fourier transform

2014 ◽  
Author(s):  
Guglielmo Frigo ◽  
Claudio Narduzzi
Keyword(s):  
Fourier Transform ◽  
Compressive Sensing ◽  
Artifact Removal ◽  
Gradient Artifact

scholarly journals Compressive Sensing in Signal Processing: Algorithms and Transform Domain Formulations

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Irena Orović ◽  
Vladan Papić ◽  
Cornel Ioana ◽  
Xiumei Li ◽  
Srdjan Stanković
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Compressive Sensing ◽  
Signal Reconstruction ◽  
Signal Acquisition ◽  
Transform Domain ◽  
Hermite Transform ◽  
Time Frequency ◽  
Reconstruction Methods ◽  
Fourier Transform Domain

Compressive sensing has emerged as an area that opens new perspectives in signal acquisition and processing. It appears as an alternative to the traditional sampling theory, endeavoring to reduce the required number of samples for successful signal reconstruction. In practice, compressive sensing aims to provide saving in sensing resources, transmission, and storage capacities and to facilitate signal processing in the circumstances when certain data are unavailable. To that end, compressive sensing relies on the mathematical algorithms solving the problem of data reconstruction from a greatly reduced number of measurements by exploring the properties of sparsity and incoherence. Therefore, this concept includes the optimization procedures aiming to provide the sparsest solution in a suitable representation domain. This work, therefore, offers a survey of the compressive sensing idea and prerequisites, together with the commonly used reconstruction methods. Moreover, the compressive sensing problem formulation is considered in signal processing applications assuming some of the commonly used transformation domains, namely, the Fourier transform domain, the polynomial Fourier transform domain, Hermite transform domain, and combined time-frequency domain.

Gradient Artifact Removal in Co-registration EEG/fMRI

2009 ◽  
pp. 1143-1146 ◽  
Author(s):  
E. Sartori ◽  
E. Formaggio ◽  
S. F. Storti ◽  
A. Bertoldo ◽  
P. Manganotti ◽  
...  
Keyword(s):  
Artifact Removal ◽  
Gradient Artifact

Optimum gradient artifact removal from EEG-data using facet

2013 ◽  
Vol 333 ◽  
pp. e622
Author(s):  
J. Glaser ◽  
V. Schöpf ◽  
R. Beisteiner ◽  
H. Bauer ◽  
F. Fischmeister
Keyword(s):  
Artifact Removal ◽  
Eeg Data ◽  
Gradient Artifact

Independent Vector Analysis for Gradient Artifact Removal in Concurrent EEG-fMRI Data

2015 ◽  
Vol 62 (7) ◽  
pp. 1750-1758 ◽  
Author(s):  
Partha Pratim Acharjee ◽  
Ronald Phlypo ◽  
Lei Wu ◽  
Vince D. Calhoun ◽  
Tulay Adali
Keyword(s):  
Vector Analysis ◽  
Fmri Data ◽  
Independent Vector ◽  
Artifact Removal ◽  
Independent Vector Analysis ◽  
Gradient Artifact

On Efficient Acquisition and Recovery Methods for Certain Types of Big Data

2016 ◽  
pp. 137-147
Author(s):  
George Avirappattu
Keyword(s):  
Big Data ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Compressive Sensing ◽  
Data Streams ◽  
Volume Velocity ◽  
Almost All ◽  
Sparse Fast Fourier Transform ◽  
Recovery Methods

Big data is characterized in many circles in terms of the three V's – volume, velocity and variety. Although most of us can sense palpable opportunities presented by big data there are overwhelming challenges, at many levels, turning such data into actionable information or building entities that efficiently work together based on it. This chapter discusses ways to potentially reduce the volume and velocity aspects of certain kinds of data (with sparsity and structure), while acquiring itself. Such reduction can alleviate the challenges to some extent at all levels, especially during the storage, retrieval, communication, and analysis phases. In this chapter we will conduct a non-technical survey, bringing together ideas from some recent and current developments. We focus primarily on Compressive Sensing and sparse Fast Fourier Transform or Sparse Fourier Transform. Almost all natural signals or data streams are known to have some level of sparsity and structure that are key for these efficiencies to take place.

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